Juliette Conrath

Stergos Afantenos

Nicholas Asher

Philippe Muller

Unsupervised extraction of semantic relations using discourse cues

This paper presents a knowledge base containing triples involving pairs of verbs associated with semantic or discourse relations. The relations in these triples are marked by discourse connectors between two adjacent instances of the verbs in the triple in the large French corpus, frWaC. We detail several measures that evaluate the relevance of the triples and the strength of their association. We use manual annotations to evaluate our method, and also study the coverage of our resource with respect to the discourse annotated corpus Annodis. Our positive results show the potential impact of our resource for discourse analysis tasks as well as other semantically oriented tasks like temporal and causal information extraction.

Introduction

Relational lexical resources, which describe semantic relations between lexical items, have traditionally focused on relations like synonymy or similarity in thesauri, perhaps including some hierarchical semantic relations like hyperonymy or hyponomy or part-whole relations as in the resource Wordnet [START_REF] Felbaum | Wordnet, an Electronic Lexical Database for English[END_REF]. Some distributional thesauri contain more varied relations, see e.g. [START_REF] Grefenstette | Explorations in automatic thesaurus discovery[END_REF], however these relations are not typed. The lexical semantics given by FrameNet [START_REF] Baker | The Berkeley FrameNet Project[END_REF] does include causal and temporal relations, as does Verbocean [START_REF] Chklovski | Verbocean : Mining the web for fine-grained semantic verb relations[END_REF]), but coverage is limited and empirical validation of these resources is partial and still largely remains to be done.

Lexical relations, in particular between verbs, are nevertheless crucial for understanding natural language and for many information processing tasks. They are needed for textual inference, in which one has to infer certain relations between eventualities [START_REF] Hashimoto | Large-scale verb entailment acquisition from the Web[END_REF][START_REF] Tremper | A discriminative analysis of fine-grained semantic relations including presupposition : Annotation and classification[END_REF], for information extraction tasks, like finding temporal relations between eventualities mentioned in a text [START_REF] Uzzaman | Semeval-2013 task 1 : Tempeval-3 : Evaluating time expressions, events, and temporal relations[END_REF], for automatic summarization [START_REF] Liu | Extractive summarization based on event term clustering[END_REF], and for discourse parsing in the absence of explicit discourse markers [START_REF] Sporleder | Using Automatically Labelled Examples to Classify Rhetorical Relations : An Assessment[END_REF].

In this paper we report on our efforts to extract semantic relations essential to the analysis of discourse and its interpretation, in which links are made between units of text or rather their semantic representations as in (1) in virtue of semantic information about the two main verbs of those clauses.

(1) The candidate demonstrated his expertise during the interview. The committee was completely convinced. We follow similar work on the extraction of causal, temporal, entailment and presuppositional relations from corpora [START_REF] Do | Minimally supervised event causality identification[END_REF][START_REF] Chambers | Unsupervised Learning of Narrative Event Chains[END_REF][START_REF] Hashimoto | Large-scale verb entailment acquisition from the Web[END_REF][START_REF] Tremper | A discriminative analysis of fine-grained semantic relations including presupposition : Annotation and classification[END_REF], though our goals and validation methods are different. While one of our goals is to use this information to improve performance in predicting discourse relations between clauses, we believe that such a lexical resource will have other uses in other tasks in which semantic information is needed.

Discourse analysis is a difficult task. Rhetorical relations are frequently implicit and require for their identification inference using diverse sources of lexical and compositional semantic information. In the Penn Discourse Treebank corpus for example, 52% of the discourse relations are unmarked [START_REF] Prasad | The Penn Discourse TreeBank 2.0[END_REF]. Accordingly, annotation with discourse structure is a slow and error prone task, and relatively little annotated data is currently available ; and so machine learning approaches have had limited success in this area. Our approach addresses this problem, using non annotated data with features that can be automatically detected to find typical contexts (pairs of discourse units) in which various discourse relations occur. We suppose with [START_REF] Sporleder | Using Automatically Labelled Examples to Classify Rhetorical Relations : An Assessment[END_REF][START_REF] Braud | Identification automatique des relations discursives "implicites" à partir de données annotées et de corpus bruts[END_REF] that such contexts display regular lexical associations, in particular with verbs in those discourse units. An explicit, manually compiled list of all possible associations between two verbs and the semantic relations they suggest is infeasible, so we present here an automatic method for compiling such a list, inspired by the Verbocean project [START_REF] Chklovski | Verbocean : Mining the web for fine-grained semantic verb relations[END_REF].

Our hypothesis, supported by existing corpora, is that adjacent clauses are often arguments of discourse relations. When these clauses contain certain adverbs or other discourse connectors, we can recover automatically one or more discourse relations that we associate with the main verbs of those clauses. We extract triples consisting of the two verbs and a semantic relation from a large corpus with the aim of inferring that such a pair of verbs can suggest the semantic relation even in the absence of an explicit discourse marker. We thus also suppose, with [START_REF] Sporleder | Using Automatically Labelled Examples to Classify Rhetorical Relations : An Assessment[END_REF][START_REF] Braud | Identification automatique des relations discursives "implicites" à partir de données annotées et de corpus bruts[END_REF], that such discourse markers are at least partially redundant ; inferring a discourse relation between two clauses relies not only the marker but on the two verbs in the related clauses as well. All of our work has been done on French data.

Our paper is organized as follows. We describe first the knowledge base of verb semantic relation triples that we have constructed (section 2) ; we then present our methods for isolating verb pairs implicating discourse or temporal information (section 3). A third section describes our methods of evaluation (section 4) and a fourth discusses related work (section 5).

Exploring relations between verbs in a corpus

We built a knowledge base (V 2 R) 1 using the frWaC corpus [START_REF] Baroni | The wacky wide web : a collection of very large linguistically processed web-crawled corpora[END_REF]. frWaC contains about 1.6 billion words and was collected on the Web on the .fr domain. We first parsed the documents in our corpus using BONSAI 2 , which first produced a morpho-syntactic labeling using MElt [START_REF] Denis | Coupling an annotated corpus and a lexicon for state-of-the-art pos tagging[END_REF] and then a syntactic analysis in the form of dependency trees via a French version of the MaltParser [START_REF] Nivre | Maltparser : A language-independent system for data-driven dependency parsing[END_REF].

Our goal is to find pairs of verbs linked by a relation explicitly marked by a discourse connector in the corpus, as an indication of a regular semantic relation between the two verbs. The relations we have considered are common to most theories of discourse analysis, and they can be grouped into four classes [START_REF] Prasad | The Penn Discourse TreeBank 2.0[END_REF] : causal (contingency) relations, temporal relations, comparison relations (mainly contrast type relations), and expansion relations (e.g. elaboration or continuation).

To find explicitly marked relations, we used a lexicon of discourse connectors for French, the manually constructed LEXCONN resource [START_REF] Roze | Lexconn : A french lexicon of discourse connectives[END_REF] 3 . LEXCONN includes 358 connectors and gives their syntactic category as well as associated discourse relations inspired from [START_REF] Asher | Logics of Conversation[END_REF]. Some connectors are ambiguous in that they are associated with several relations. We used only the unambiguous connectors (263 in all) in LEXCONN, as a first step. We regrouped the LEXCONN relations into classes 4 : explanation relations (parce que/because) and result (ainsi/thus) form the causal class ; temporal relations (puis, après que/then,after that) form the narration group. We also considered other relations like contrast (mais/but), continuation (et, encore/and,again), background (alors que/while), temporal location (quand, pendant que/when), detachment (de toutes façons/anyway), elaboration (en particulier/in particular), alternation (ou/or), commentary (au fait/by the way), rephrasing (du moins/at least), and evidence (effectivement/indeed).

We searched our syntactically parsed corpus for connectors. When a connector is found and its syntactic category verified, if it is close enough to the root of the sentence (at most one dependency link from the root), we look for an inter-sentential link. The first verb of our pair corresponds in this case to the last verb of the previous sentence in the case of connectors for narration, or to its main verb for all the other relations. We search for the second verb in the pair within a window of two dependency links after the connector. If the connector is not close enough to the root of the sentence, we look for a intra-sentential link. In this case, we look for the two verbs of the pair in the same sentence within a forward and backward window of two dependency links.

If two verbs are found, we examine their local context to better characterize their usage and to improve our results. If one of the verbs is a modal or support verb, we look for the verb dependent on the modal or support verb and use that as the verb in our pair (if it exists), while keeping the presence of the support verb in memory. Unlike support verbs, we use the presence of a negation or a reflexive particle in the local context to distinguish verbs with different meanings ; e.g., comprendre/understand vs. ne pas comprendre/not understand, agir/act vs. s'agir/concern are all distinct entries. To get at different verb senses, we search for idiomatic usage of prepositions using the Dicovalence resource (Van Den Eynde and Mertens, 2010), which contains valency frames for more than 3700 simple French verbs. We also use the Lefff resource [START_REF] Sagot | The lefff, a freely available and large-coverage morphological and syntactic lexicon for french[END_REF] to find idiomatic verbal locutions. We also encode other information that do not lead to distinct lexical entries : tense, and voice.

Once (verb1, verb2, relation). Given that we have used only unambiguous connectors (so classified by LEXCONN), the association of a relation with a connector is immediate. We associate to each triple type the number of intra-sentential, intersentential and total number of occurrences. The other features mentioned above are stored in a separate table.

Our method has isolated more than 1 million distinct types of triples for V 2 R and 2 million occurrences, of which 95% are intra-sentential5 . Among these triples, 6.2% have 5 or more occurrences.

Table 1 summarizes the distribution of triples by relation in V 2 R. Note that triples with contrast and causal relations comprise the majority. This does not mean that these are the most frequent relations in the corpus but only that they are the most frequently marked by the connectors we considered. This makes for a very different distribution than that of the French manually annotated discourse corpus Annodis [START_REF] Afantenos | An empirical resource for discovering cognitive principles of discourse organisation : the ANNODIS corpus[END_REF].

Measuring the association of a pair of verbs with a relation

In the last section we presented our extraction method. We now present the measures we have used to rank verb pairs with respect to the strength of their association with a particular discourse relation. We adapted versions of standard lexical association measures like PMI (pointwise mutual information) and their variants, as well as some measures specific to the association of a causal relation between items [START_REF] Do | Minimally supervised event causality identification[END_REF]. We also experimented with a new measure specifically designed for our knowledge base.

Measures of lexical association used in research on co-occurrences in distributional semantics pick out significant associations, taking into account the frequency of the related items. We examined over 10 measures ; we discuss the ones with the best results (see section 4). One simple measure, PMI, and its variants, normalized, local [START_REF] Evert | The statistics of word cooccurrences[END_REF], discounted [START_REF] Lin | Concept discovery from text[END_REF], which are designed to reduce biases in the original measure, work well. The idea behind PMI is to estimate whether the probability of the co-occurrence of two items is greater than the a priori probability of the two items appearing independently. In distributional semantics, the measure is also used to estimate the significance of two items co-occurring with a particular grammatical dependency relation like the subject or object relation between an NP and a verb. This use of PMI measures over triples in distributional semantics fits perfectly with our task of measuring the significance of triples consisting of a pair of verbs and a particular semantic or discourse relation ; our PMI measures estimate whether the co-occurrence of two items with a particular discourse relation is higher than the a priori probability of the three items occurring independently. Our measures consider co-occurrences of two lexical items in a certain relation denoted by an explicit discourse marker. PMI and normalized PMI are defined as :

PMI = log( P (V 1 , V 2 , R) P (V 1 ) × P (V 2 ) × P (R) ) PMI _normalized = PMI -2 log(P (V 1 , V 2 , R))
Indeed, when we have a complete co-occurrence of the three items, we have :

P (V 1 ) = P (V 2 ) = P (R) = P (V 1 , V 2 , R), and PMI = -2 log(P (V 1 , V 2 , R)).
The values of normalized PMI lie between -1 and 1, approaching -1 when the items never appear together, taking the value 0 in the case of independence, and the value 1 when they always appear together. We also considered a weighted PMI measure [START_REF] Lin | Concept discovery from text[END_REF] that corrects the bias of PMI for rare triples.

A specificity measure [START_REF] Seyed | Enforcing subcategorization constraints in a parser using sub-parses recombining[END_REF], originally used to measure the precision of subcategorization frames, also performed well :

specificity = 1 3 × ( P (V 1 , V 2 , R) i P (V 1 , V i , R) + P (V 1 , V 2 , R) i P (V i , V 2 , R) + P (V 1 , V 2 , R) i P (V 1 , V 2 , R i ) )
A version of [START_REF] Do | Minimally supervised event causality identification[END_REF]'s measure for triples involving causal relations did not fare so well on other types of relation. The definition of the measure can be found in [START_REF] Do | Minimally supervised event causality identification[END_REF]. 6Finally, we investigated a measure that evaluates the contribution of each element in the triple to the significance measure (this measure is similar to specificity).

W combined (V 1 , V 2 , R) = 1 3 (w V 1 + w V 2 + w R ) with : w V 1 = P (V 1 ,V 2 ,R) max i (P (V i ,V 2 ,R)) , w V 2 = P (V 1 ,V 2 ,R) max i (P (V 1 ,V i ,R))
, and w R =

P (V 1 ,V 2 ,R) max i (P (V 1 ,V 2 ,R i )) .

Evaluating extracted relations

We evaluated V 2 R in several ways ; we provided : (i) an intrinsic evaluation of the relations between verbs (section 4.1) and (ii) an extrinsic evaluation where we evaluated the coverage of the resource on a discourse annotated corpus and its potential to help in predicting discourse relations in contexts with no explicit marking (section 4.2).

Intrinsic evaluation

Our intrinsic evaluation first evaluates the feasibility of assigning an "inherent" semantic link to a verb pair, independently of any linguistic context. For example, is it possible to judge that there is a typical causality link between push and fall, in scenarios where they share some arguments (subject, object, ...), these scenarios being left to the annotator's imagination (section 4.1.1). In a second stage, we selected several verb pairs linked with different relations in V 2 R, and 40 contexts in which these verbs occur together in the original corpus, to judge the semantic link in context (section 4.1.2).

In both cases we restricted the study to three relation groups : causal, contrastive, and narrative. These are the most often marked relations and correspond to different types of links with a meaningful semantic aspect (as opposed to the "continuation" relation for instance, which is often marked too).

Out of context evaluation

For out of context judgments, we adopted the following protocol : one of the authors chose for each relation 100 verbs with equivalent proportions of good and bad normalized PMI scores. Then the other three authors judged the validity of associating each of the 300 pairs with the corresponding relation, without any knowledge of the source of these pairs.

We measured the inter-annotator agreements with Cohen's Kappa [START_REF] Carletta | Assessing agreement on classification tasks : the kappa statistic[END_REF], which resulted in : 0.17 for cause, 0.42 for narration and 0.56 for contrast as mean values. If a 0.6 kappa serves a measure for a feasible semantic judgment task, out of context judgments appear very difficult, with only contrastive pairs as a relative exception. We decided to only consider judgments about contrast, after an adjudication phase, and we evaluated the measures presented in section 3 to see if they could discriminate between the two verb groups, those judged positively or negatively according to human annotations. A Mann-Whitney U statistical test showed all of our measures to be discriminative, with the exception of raw co-occurrence counts for which p>0.05.

In context evaluation

We also judged associations in context.

Verb This task was easier and also gave more fine-grained results, because with it we can quantify the degree of association, and the typicality of the link, as a proportion of contexts where the two verbs appear together in a given semantic relation. We can then observe if this proportion is correlated with the association measures we already presented. Nevertheless, this is a costly way of evaluating a verb pair, as we require a number of judgments on each pair. It is also not easy to sample the possible pairs with different values to be able to observe significant correlations, because we cannot predict in advance how they will be judged by the annotators.

We selected 40 contexts for each of the 15 pairs of verbs we chose, 5 for each of the target relation (cause, narration, contrast). Selected pairs range over different values of normalized PMI, again chosen by one of the authors independently of the others, who annotated the 600 contexts. Prior to adjudication, raw agreement was 78% on average, for an average kappa of 0.46 (and a maximum of 0.49). These values seem moderately good, as the task is also rather difficult.

Table 2 shows the results after adjudication : for each pair, the proportion of contexts in which the considered relation is judged to appear.

We computed two correlation values between the association ratio in contexts manually annotated and each association measure considered : one based on all annotated contexts, and one on the subset of contexts devoid of explicit markers of a semantic relation (implicit contexts). The latter is important to quantify the actual impact of the method, since explicit marking is already used as the basis of verb association in the same corpus. Implicit contexts, however, never appeared in the computation of the verb pair associations. Table 3 shows that mutual information measures are well correlated with human annotations, and that our W_combined seems useful too. We also observed results on each relation separately, although one should be careful drawing conclusions from these results since the correlations are then computed on 5 points only. These results (not shown here) show a lot of variation between relations. The U_do measure, designed for causal relations, does indeed produce good results for these relations, but does not generalize well to our other chosen relations.

Also, local PMI seems to work very well on narration and causal relations. This needs to be confirmed with more verb pairs.

We conclude that the best three measures are : normalized PMI, specificity, and W_combined. The last two assign their maximal value to several pairs, so we used them in a lexicographical ordering to sort all associated pairs, using normalized PMI to break ties. Table 4 shows the best triples with our lexicographical ranking.

Verb pair

Extrinsic evaluation

In order to evaluate the performance of our resource relative to its main intended applicationpredicting rhetorical relations in text, we intend to use our association measures as additional features to an inductive prediction model. Whether this evaluation produces results depends on the proportion of cases in which this information could help and on the coverage of our resource with respect to these cases. We used the Annodis corpus [START_REF] Afantenos | An empirical resource for discovering cognitive principles of discourse organisation : the ANNODIS corpus[END_REF], a set of French texts annotated with rhetorical relations, for our study.

To improve existing models, a significant number of the predictions to be made must involve a verb pair for which we have information in the resource. A first indication of its usefulness is also that the verb pair appears most frequently with the relation group to which the annotation belongs, for instance the fact that two verbs are related with a causal relation whenever we want to predict an explanation. This is interesting only in the absence of an explicit marking of the target relation, i.e for implicit relations.

Beyond that, it should be interesting to use all the available information about other semantic relations too : for instance a potential causal link between two events could indicate the relevance of a temporal link for the prediction of a relation. We relied again on the Lexconn marker database. As an approximation we considered that a relation between two discourse units is explicit when a Lexconn marker is present in any of the two segments, and one of the potential senses of the marker is the annotated relation. This may overestimate the number of explicit instances but ensures that all implicit instances are indeed implicit (assuming a good enough coverage of the marker resource). The Annodis corpus lists rhetorical relations between elementary discourse units (EDUs), typically clauses, and complex discourse units (sets of EDUs) ; as a simplification we only consider EDUs, since the question of what is a main verb of a complex unit is difficult to answer. This is a relatively small corpus, as it includes about 2000 instances of relations between elementary discourse units.

Table 5 present results for coverage, for the main relations in the annotated corpus. Note that only a small part of the set of relations between EDUs is considered when we restrict instances to both EDUs with verbs (about 20% of the whole). It turns out that a lot of EDUs in Annodis are short segments (incises, detached segments, ... Our table includes : the proportion of verb pairs found in Annodis EDUs that appear in V 2 R, the proportion of triples from Annodis that appear in V 2 R (with the correct relation), and the restriction of these proportions to implicit contexts in Annodis. Except for a few exceptions due to lemmatisation errors, all verbs in Annodis are in V 2 R in at least one pair, and we can see that the pairs in V 2 R cover most of the pairs appearing in Annodis (almost 70% globally and between 60 and 80% depending on the relation), and a little less of implicit cases (around 55% on average). We note that a high proportion of the implicit cases contains verb pairs that have been collected in a marked context, even for rarely marked relations like elaboration or continuation-contexts with these relations are the majority in Annodis. Furthermore more than half of these contexts are associated with the right relation in V 2 R. Thus the hypothesis of the partial redundancy of connectors appears useful when isolating verbal associations relevant for discourse from a large corpus. We also looked at semantic neighbors of the verbs in V 2 R but this did not increase coverage significantly.

A good test of the predictive power of the semantic information we gathered is also to include the association measures as additional features to a predictive model, to improve classically low results on implicit discourse relations. The only available discursive corpus in French, Annodis, is small, and as shown above only about 400 instances have a verb in both related EDUs. We trained and tested a maximum entropy model with and without the association measures as features, on top of features presented in [START_REF] Muller | Constrained decoding for textlevel discourse parsing[END_REF], who trained a relation model on the same corpus. We did a 10-fold cross-validation on the 400 instance subset as evaluation, and did not find a significant difference between the two set-ups (F1 score was in the range .40-.42, similar to the cited paper), which is unsurprising given the size of the subset. We plan to evaluate our method relative to discourse parsing by building an English resource like V 2 R ; we will then be able to use the much larger PDTB corpus (10 times as large as Annodis) as a source of implicit discourse relations. This should prove a much more telling evaluation of the usefulness of association measures in predicting implicit discourse relations.

Related work

There are two different groups of related work. The first group aims to alleviate the lack of annotated data for discourse parsing by using a weakly supervised approach, exploiting the presence of discourse connectors in a large non-annotated corpus. Each pair of elementary discourse units is automatically annotated with the discourse relation triggered by the presence of the connector (connectors are often filtered for non-discursive uses). Those connectors are afterwards eliminated from the corpus so that the model trained on this dataset will not be informed by the presence of those connectors. The pioneering article in this group is [START_REF] Marcu | An Unsupervised Approach to Recognizing Discourse Relations[END_REF]. Such learning methods with such "artificial data" obtain low scores, barely above chance as shown in [START_REF] Sporleder | Using Automatically Labelled Examples to Classify Rhetorical Relations : An Assessment[END_REF]. [START_REF] Braud | Identification automatique des relations discursives "implicites" à partir de données annotées et de corpus bruts[END_REF] observe that the performance of a classifier for the prediction of implicit relations is much lower when using "artificial" data than on "natural" data (implicit relations annotated by a human being). They propose a method which exploits these two different kinds of datasets together in various mixtures and on the level of the prediction algorithm, obtaining thus a significant improvement on the Annodis corpus. Our approach is different and complementary ; we isolate the semantic relations between pairs of verbs. We can use that as a feature on discourse units for discourse parsing but it has other uses as well.

A second group aims at identifying discourse relations (implicit or not) by focusing on the use of finegrained lexical relations as another feature during the training phase. Most of this work focuses mainly on the use of lexical relations between two verbs. [START_REF] Chklovski | Verbocean : Mining the web for fine-grained semantic verb relations[END_REF], for example, rely on specific patterns constructed manually for each semantic relation between (similarity, strength, antonymy, enablement and temporal happens-before). They use the web as a corpus in order to estimate the PMI between a pattern and a pair of verbs (a precise measurement cannot be achieved over the web since the probability of a pattern is not precisely known over all the web). A threshold on the value of the PMI (manually fixed) permits thus to determine the pairs of verbs that are related to the relation denoted by the pattern. In the same spirit, Kozareva (2012) is using a weakly supervised approach for the extraction of pairs of verbs that are potentially implied in a cause-effect relation. Her method consists in using patterns applied to the web in order to extract pairs and generate new seeds. [START_REF] Do | Minimally supervised event causality identification[END_REF] focus on causal relations and take into account not only verbs but also event denoting nouns. According to this paper, an event is denoted by a predicate with a specific number of arguments and thus the association of the events is the sum of the association between predicates, between predicates and arguments and between arguments. Their association measures are based on PMI and are quite complex. Our results show that their measures do not generalize well to association with all discourse relations. Using Gigaword as a corpus and a reimplementation of [START_REF] Lin | A PDTB-styled end-to-end discourse parser[END_REF] they have extracted discourse relations. An inductive logic programming approach is finally used exploiting the interaction between causal pairs and discourse relations in order to extract causal links. Those papers focus on specific relations with the exception of [START_REF] Chklovski | Verbocean : Mining the web for fine-grained semantic verb relations[END_REF] who do not present a systematic evaluation of their results. An important difference of our approach is also to consider predicates and their negation as separate entries.

Finally, we mention the approaches which while focusing on the learning of discourse structures, nonetheless enrich their systems with lexical information. Feng and Hirst (2012) have used HILDA [START_REF] Hernault | HILDA : A Discourse Parser Using Support Vector Machine Classification[END_REF] adding more features. A specific family of features represents lexical similarity based on the hierarchical distance in VERBNET and WORDNET. In a similar fashion, [START_REF] Wellner | Classification of discourse coherence relations : an exploratory study using multiple knowledge sources[END_REF] focus on intra-sentential discourse relations adding lexical information on the features based on measures proposed by Lin (1998) calculated on the British National Corpus. Those approaches use thus only information on lexical similarity without semantically typing this link. The impact of this information seems limited. As far as evaluation is concerned, our method is similar to that followed in [START_REF] Tremper | A discriminative analysis of fine-grained semantic relations including presupposition : Annotation and classification[END_REF] for implication relations combining in and out of context evaluation for verbal associations. Their inter-annotator agreement is similar to ours (0.42-0.44 of Kappa) with very different choices : the anno-tators were supposed to discriminate verbal links between the different possible sub-cases. The pairs of verbs were identified by the system of Lin and Pantel. These authors also present a classification model among the different types of relationships, assuming that two verbs are semantically related.

Conclusions

We have presented a knowledge base of triples involving pairs of verbs associated with semantic or discourse relations. We extracted these triples from the large French corpus, frWaC, using discourse connectors as markers of relations between two adjacent clauses containing verbs. We investigated several measures to give the strength of association of a pair of verbs with a relation. We used manual annotations to evaluate our method and select the best measures, and we also studied the coverage of our resource on the discourse annotated corpus Annodis. Our positive results show our resource has the potential to help discourse analysis as well as other semantically oriented tasks.

  we have obtained a list of verb pairs associated with

	Relation	Distribution
	contrast	50,104%
	cause	33,108%
	continuation	8,243%
	narration	6,362%
	background	1,853%
	temporal localisation	0.177%
	detachement	0.149%
	elaboration	0.002%
	alternation	0.002%
	TABLE 1 -Distribution of relations in
	V 2 R ;commentary, reformulation and
	evidence occur with negligible fre-
	quency.

a connector, we aggregate this data to get a list of triple types

TABLE 4 -

 4 Ten best triples in the database.

		Translation	Relation
	abandonner / mener	abandon / lead	background
	ne pas s'arrêter / rouler	not stop / drive	narration
	donner satisfaction sur / réélire give satisfaction concerning / re-elect continuation
	emporter / ne pas cesser	take away / not stop	summary
	emprunter / assurer	borrow / insure	cause
	ne pas manquer / prolonger	not miss / prolong	detachment
	ratifier / trembler	ratify / tremble	background
	avoir honte / faire pitié	be ashamed / cause pity	cause
	avoir droit / cotiser pour	be entitled / contribute to	temploc
	ne pas représenter / stéréotyper not represent / stereotype	temploc

TABLE 5 -

 5 ). Coverage of verb pairs in V 2 R with respect to EDU pairs in the Annodis corpus containing two verbs. Except for the first line, all numbers are percentages. Pair = verb pairs in the EDUs linked by a rhetorical relation R, Triple=verb pair associated with a relation R in V 2 R, BG = Background, cont.=continuation, elab.=elaboration.

		global narration cause contrast elab. cont. BG other
	Annodis pairs	427	73	67	41	96	92	24	16
	Annodis pairs ∈ V 2 R	68.9	71.2	70.8	78.0 68.3 61.9 74.1	62.5
	Annodis triples ∈ V 2 R	26.5	34.2	50.0	70.7	0.0 20.6 11.1	0.0
	Implicit Annodis pairs	83.4	71.2	79.2	36.6 99.0 94.8 88.9 100.0
	Implicit Annodis pairs ∈ V 2 R	56.9	52.1	54.2	31.7 67.3 58.8 66.7	62.5
	(any relation)
	Implicit Annodis triples ∈	17.7	24.7	40.3	31.7	0.0 19.6 11.1	0.0
	V 2 R (with correct relation)

The low proportion of inter-sentential occurrences comes from our conservative scheme for finding these occurrences, which uses only those connectors at the beginning of the second sentence. Other schemes are possible but would, we fear, introduce too much noise into the data.

We simplified their measure by ignoring IDF (inverse document frequency) and the distance between the verbs, as neither measure applies to our task.

This work has been supported by the French agency Agence Nationale de la Recherche (ANR-12-CORD-0004).

Available as an SQLite

database at https://dl.dropboxusercontent.com/u/78938139/


Jürgen Sturm

Stéphane Magnenat

Nikolas Engelhard

Franc ¸ois Pomerleau

Francis Colas

Daniel Cremers

Roland Siegwart

Wolfram Burgard

Towards a benchmark for RGB-D SLAM evaluation

We provide a large dataset containing RGB-D image sequences and the ground-truth camera trajectories with the goal to establish a benchmark for the evaluation of visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor and the groundtruth trajectory of camera poses. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.

I. INTRODUCTION

Simultaneous localization and mapping (SLAM) has a long history in robotics and computer-vision research [START_REF] Lu | Globally consistent range scan alignment for environment mapping[END_REF], [START_REF] Jin | Real-time 3-D motion and structure of point features: Front-end system for vision-based control and interaction[END_REF], [START_REF] Dellaert | Square root SAM[END_REF], [START_REF] Olson | Fast iterative optimization of pose graphs with poor initial estimates[END_REF], [START_REF] Klein | Parallel tracking and mapping for small AR workspaces[END_REF], [START_REF] Grisetti | Non-linear constraint network optimization for efficient map learning[END_REF]. Different sensor modalities have been explored in the past, including 2D laser scanners [START_REF] Montemerlo | FastSLAM: A factored solution to the simultaneous localization and mapping problem[END_REF], [START_REF] Grisetti | Improved techniques for grid mapping with rao-blackwellized particle filters[END_REF], 3D scanners [START_REF] Nüchter | 6D SLAM -3D mapping outdoor environments: Research articles[END_REF], [START_REF] Pitzer | Automatic reconstruction of textured 3D models[END_REF], monocular cameras [START_REF] Nistér | Preemptive ransac for live structure and motion estimation[END_REF], [START_REF] Klein | Parallel tracking and mapping for small AR workspaces[END_REF], [START_REF] Konolige | Towards lifelong visual maps[END_REF], [START_REF] Strasdat | Scale drift-aware large scale monocular slam[END_REF], [START_REF] Stühmer | Real-time dense geometry from a handheld camera[END_REF] and stereo systems [START_REF] Konolige | Outdoor mapping and navigation using stereo vision[END_REF]. Recently, low-cost RGB-D sensors became available, of which the most prominent one is the Microsoft Kinect. Such sensors provide both color images and dense depth maps at video frame rates. Henry et al. [START_REF] Henry | RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments[END_REF] were the first to use the Kinect sensor in a 3D SLAM system. Others have followed [START_REF] Engelhard | Realtime 3D visual SLAM with a hand-held RGB-D camera[END_REF], and we expect to see more approaches using RGB-D data for visual SLAM in the near future.

Various datasets and benchmarks have been proposed for laser-and camera-based SLAM, such as the Freiburg, Intel and Newcollege datasets [START_REF] Stachniss | Laser-based slam datasets and benchmarks at[END_REF], [START_REF] Smith | The new college vision and laser data set[END_REF]. However until now, no suitable dataset or benchmark existed that can be used to evaluate, measure, and compare the performance of RGB-D SLAM systems. As we consider objective evaluation methods to be highly important for measuring progress in the field (and demonstrating this in a verifiable way), we decided to provide such a dataset. To the best of our knowledge, this is the first RGB-D dataset for visual SLAM benchmarking. {engelhar,burgard}@informatik.uni-freiburg.de

II. EXPERIMENTAL SETUP AND DATA ACQUISITION

We acquired a large set of data recordings containing both the RGB-D data from the Kinect and the ground truth estimates from the mocap system. We moved the Kinect along different trajectories in typical office environments (see Fig. 1a). The recordings differ in their translational and angular velocities (fast/slow movements) and the size of the environment (one desk, several desks, whole room). We also acquired data for three specific trajectories for debugging purposes, i.e., we moved the Kinect (more or less) individually along the x/y/z-axes and rotated it individually around the x/y/z-axes.

We captured both the color and depth images from an off-the-shelf Microsoft Kinect sensor using PrimeSense's OpenNI-driver. All data was logged at full resolution (640×480) and full frame rate (30 Hz) of the sensor on a Linux laptop running Ubuntu 10.10 and ROS Diamondback. Further, we recorded IMU data from the accelerometer in the Kinect at 500 Hz and also read out the internal sensor parameters from the Kinect factory calibration.

Further, we obtained the camera trajectory by using an external motion capturing system from MotionAnalysis at 100 Hz (see Fig. 1b). We attached reflective targets to the Kinect (see Fig. 1c) and used a modified checkerboard for calibration (Fig. 1d) to obtain the transformation between the optical frame of the Kinect sensor and the coordinate system of the motion capture system. Finally, we also video-taped all recordings with an external video camera to capture the camera motion and the environment from a different view point.

The original data has been recorded as a ROS bag file. In total, we collected 50 GB of Kinect data, divided into separate nine sequences. The dataset is available online under the Creative Commons Attribution license at https://cvpr.in.tum.de/research/ datasets/rgbd-dataset

The website contains-next to additional information about the data formats-videos for simple visual inspection of the dataset.

III. EVALUATION

For evaluating visual SLAM algorithms on our dataset, we propose a metric similar to the one introduced by [START_REF] Kümmerle | On measuring the accuracy of SLAM algorithms[END_REF]. The general idea is to compute the relative error between the true and estimated motion w.r.t. the optical frame of the RGB camera. As we have ground-truth pose information for all time indices, we propose to compute the error as the sum of distances between the relative pose at time i and time i + ∆, i.e.,

error = n i=1 [(x i+∆ xi ) (x i+∆ x i )] 2 (1)
where i = 1, . . . , n are the time indices where ground truth information is available, ∆ is a free parameter that corresponds to the time scale, x i is the ground truth pose at time index i, xi the estimated pose at time index i, stands for the inverse motion composition operator. If the estimated trajectory has missing values, i.e., there are timesteps i j1 , . . . , i jm for which no pose xi could be estimated, the ratio of missing poses m/n should be stated as well.

All data necessary to evaluate our measure are present in the dataset. We plan to release a Python script that computes these measures automatically given the estimated trajectory and the respective dataset. To prevent that (future) approaches are over-fitted on the dataset, we recorded all scenes twice, and held back the ground-truth trajectory in these secondary recordings. With this, we plan to provide a comparative offline evaluation benchmark for visual SLAM systems.

IV. CONCLUSIONS

In this paper, we have presented a novel RGB-D dataset for benchmarking visual SLAM algorithms. The dataset contains color images, depth maps, and associated ground-truth camera pose information. Further, we proposed an evaluation metric that can be used to assess the performance of a visual SLAM system. We thus propose a benchmark that allows researchers to objectively evaluate visual SLAM systems. Our next step is to evaluate our own system [START_REF] Engelhard | Realtime 3D visual SLAM with a hand-held RGB-D camera[END_REF] on this dataset in order to provide a baseline for future implementations and evaluations. In this way, we hope to detect (and resolve) potential problems present in our current dataset, such as calibration and synchronization issues between the Kinect and our mocap system as well as the effects of motion blur and the rolling shutter of the Kinect. Furthermore, we want to investigate ways to measure the performance of a SLAM system not only in terms of the accuracy of the estimated camera trajectory, but also in terms of the quality of the resulting map of the environment.

Fig. 1 :

 1 Fig. 1: The office environment and the experimental setup in which the RGB-D dataset with ground truth camera poses was recorded.


Special Issue on Robot Vision Jana Košecká Eric Marchand Peter Corke

The International Journal of Robotics Research (IJRR) has a long history of publishing the state-of-the-art in the field of robotic vision. This is the fourth special issue devoted to the topic. Previous special issues were published in 2012 (Volume 31,No. 4), 2010 (Volume 29, Nos 2-3) and 2007 (Volume 26, No. 7, jointly with the International Journal of Computer Vision). In a closely related field was the special issue on Visual Servoing published in IJRR, 2003 (Volume 22, Nos 10-11). These issues nicely summarize the highlights and progress of the past 12 years of research devoted to the use of visual perception for robotics.

Looking back across these issues we see perennial topics such as calibration; feature detection, description and matching; multi-view geometry; and filtering and prediction. Of course for robotic vision we have also seen many papers with a strong control focus and also a focus on high-speed operation. Perennial challenges over that period, perhaps still open problems, include robustness and vision-guided manipulation. Happily, many techniques have matured over this period and become an integral part of many robotic vision systems, for example visual odometry, visual Simultaneous Localization and Mapping (SLAM), visual place recognition and the fusion of vision with other sensors, most notably inertial sensors. This period has truly seen amazing technological change, not just the constant progress due to Moore's law but major innovations such as fieldprogrammable gate arrays (FPGAs) and graphics processing units (GPUs), mobile computing architectures, low-cost high-performance inertial sensors and RGB-D sensors. Many of these have been driven by demand for consumer products such as smartphones and games, but have also provided a rich bounty for roboticists. The ready availability of capable low-cost off-theshelf robotic platforms for domains such as underwater autonomous unmanned vehicles (AUVs), flying unmanned aerial vehicles (UAVs) and humanoid robots, all of which could usefully use vision sensors, is also helping to advance the field. Finally, the staple of all robotic vision systems, the camera, is evolving in very interesting directions. We now have cameras that are small, cheap and lightweight, that have progressive scan and global shutters, high dynamic range, high frame rate and wide fields of view obtained by catadioptrics or by multiple cameras with stitched imagery.

High-quality open-source software for robotic vision such as ROS and OpenCV has matured and gained wide currency over the period. The last few years has also seen machine learning techniques demonstrate ever-growing performance for a wide range of applications but particularly in vision-based problems such as object recognition and scene understanding.

Taken together these advances have significantly lowered the time and cost entry barriers for tackling and advancing important vision and robot perception problems. They have also opened avenues for new applications, enabled more sophisticated robotic tasks and more robust deployment of visual sensing in larger scale settings.

Interest in robotic vision is growing rapidly, as evidenced by the number of technical sessions in our leading conferences (ICRA, IROS, RSS and ISRR) and also by the number of submissions to this special issue. The call for papers for this issue was issued in late 2013 and a total of 47 papers were submitted (a record for the robot vision special issue). After a thorough review process we have accepted 15 papers for publication, which will appear as a double issue. Seven of these papers also feature multimedia contents.

The first two papers are concerned with humanoid robotics. Research in this area is now quite mature with respect to control issues and it is now time to close the perception-action loop. The first paper by Garcia, Stasse, Hayet, Dune, Esteves and Laumond proposes a visual servoing scheme to control dynamic walking of the HRP2 humanoid robot. The main idea of this paper is to incorporate the visual error (three-dimensional (3D) information in the proposed method) into a model-predictive control formulation for walking pattern generation. The approach proves to be more efficient than decoupling the computation of a reference velocity that is given by the pattern generator. The proposed approach is validated in simulation and we look forward to seeing the full validation of this control scheme on the actual HRP2 in future.

Calibration has always been an important issue in robot vision. The paper by Birbach, Frese and Baüml addresses the problem of inertial measurement unit (IMU)-StereoCamera calibration with respect to the robot's kinematic chain (head, torso and wrist). As part of the calibration process, the intrinsic and extrinsic parameters of the sensors (stereo cameras, Kinect sensor, IMU) in the robot's head are determined. The calibration process is validated with the dynamic task of ball catching using onboard visual sensing as well as mobile manipulation.

For natural human-robot interaction (HRI) vision is important but other senses, sound and vision, warrant consideration. Alameda-Pineda and Horaud propose combining vision-based HRI with robot hearing and audio-based HRI in an approach to detect and localize people that are both seen and heard. Localizing the speaker not only allows for better speech or speaker recognition by reducing the signal-to-noise ratio but also eases the tracking in the image stream. Audio-cues are generated by inter-aural time delay between stereo microphones, and visual signals are generated by stereo vision interest points and face detection. The system is extensively described and tested on publicly available datasets and the NAO humanoid robot. This work could be applied to the identification of human activity, for example, counting the number of speakers, localizing them, assessing their speaking state and so on.

As for previous special issues, many visual SLAM papers were submitted, and four are included here. SLAM is now routine for a vision-based robotics system and has now been successfully deployed into consumer robots. The SLAM papers published in this special issue not only provide methodological contributions but actual and efficient systems that are deployed in the field.

The paper by Kim and Eustice addresses a very important issue in SLAM and especially underwater SLAM, where visual features are sparse and imaging conditions challenging. It links perception and action in a meaningful way for localization and exploration problems. Whereas in a conventional approach, SLAM is passive and performed on preplanned trajectories, here the navigation is considered concurrently with the SLAM and is computed, using a decision theoretic approach, with two competitive objectives -efficient map coverage and minimum overlap -in order to avoid drift and bound the navigation error. A real-world experiment of this visual SLAM system for the task of autonomous underwater ship hull inspection is presented.

Camera localization is a fundamental issue in vision-based robotics. The paper by Lim, Sinha, Cohen, Uyttendaele and Kim details an algorithm for real-time localization of a moving monocular camera with respect to a previously generated 3D point cloud (using a structure from motion approach). A key originality of the approach is the ability to use the algorithm on an embedded computer with low computing power and to distribute computation time over several frames. Rather than considering online extraction of scale-invariant descriptors, an indexed database containing multiple descriptors per 3D point extracted at multiple scales is constructed offline. Computational efficiency is achieved by using these descriptors in an active matching approach during frame-to-frame tracking. The authors introduce several important new techniques to present a well thought out and very efficient system.

The work of Zhao, Huang, Sun, Yan and Dissanayake proposes a novel parameterization for bundle adjustment based on parallax angles for SLAM applications. The new modeling takes into account the parallax of the image point features rather than the more common depth parameter. They show that there are situations, such as when the camera motion is aligned with the direction of the features or when these features are far away, where existing parameterizations (such as the inverse depth parameterization) lead to poor conditioning in the optimization problem. The proposed formulation is shown to be much better conditioned, leading to improved motion and structure estimates. Results are presented for a number of standard datasets and opensource code is provided.

The paper by Kim, Yoon and Kweon also considers navigation using visual SLAM. With respect to the previous SLAM papers, the approach taken in this paper emphasizes key frame-based camera tracking and 3D mapping using a Bayesian filtering framework. The authors make the observation that, when using motion estimation from visual odometry to predict the new frame pose, the process noise is no longer independent of measurement noise if the same observations have been used in visual odometry and in SLAM. To solve this issue, that is, ensuring that process and measurement noises are independent, the available measurements are divided into two sets that are exclusively used by each process. The authors proceed by developing a Rao-Blackwellized particle filter formulation for this segmentation of the measurement set using, for computational efficiency, a FastSLAM-like process.

Robotic vision has definitively reached a mature level when a robot controlled by vision sensors can be deployed in the field. The paper by Hansen, Alismail, Rander and Browning deals with an important industrial issue related to gas pipeline inspection. A visual odometry-based system uses calibrated fish-eye imagery and sparse structured lighting to produce 3D textured surface models of the pipe's internal surface using a sparse bundle adjustment framework. This paper describes a well-integrated reconstruction system and significant experimentation is presented.

The work of Guizilini and Ramos proposes a technique for learning an automatic segmentation of static and dynamic objects in monocular sequences in an unsupervised setting. The approaches build upon techniques from monocular motion estimation and robust matching, and classify feature tracks as static or dynamic by incrementally training a Gaussian process classifier. This suggests a promising approach for learning models of semantically meaningful objects in an unsupervised setting, as well as enabling this strategy to attain reliable visual odometry estimates in the presence of moving objects.

The paper by Cadena and Košecká tackles the problem of simultaneous segmentation and categorization in RGBD data, by effectively and efficiently segmenting the commonly occurring background classes in indoors environments such as ground, structure, furniture and props. This initial labeling is then sampled and predictions of the locations of generic object instances are made. The problem is cast in the Conditional Random Field framework and evaluated extensively in indoors environments. This paper presents a new avenue for obtaining hypotheses about previously unseen objects that can be exploited in object search and follow-up categorization.

The paper by Whelan, Kaess, Johannsson, Fallon, Leonard and McDonald describes a complete system to generate large-scale RGB-D sensor-based reconstructions of indoor environments. The proposed method is based on volumetric fusion of depth maps. The focus is on real time (30 Hz on a GPU) for all the component subtasks, such as camera tracking, mesh construction and pose graph optimization, which is achieved by making strong use of incremental updates. The paper presents impressive results for a state-of-theart problem.

In the paper by Teo, Fermüller and Aloimonos a method for object detection and categorization using object shape in a cluttered indoor environment is proposed. The authors first detect likely location objects using the so-called torque operator, and this is followed by a novel representation of contours that enable effective multi-scale matching and grouping. The performance is evaluated on several diverse datasets containing a variety of object categories. The method is further improved when depth information is available from an RGB-D sensor. This approach is applicable to table-top setting categorization, can tolerate a large amount of clutter, occlusion and viewpoint changes and is effective for objects with discriminative shape characteristics.

The paper by Kim and Ueda deals with an important but little-studied problem of removing motion blur for fast-moving eyes. Saccades are very fast eye motions that allow humans to compensate for their limited field of view. Here a very fast lead zirconate titanate-driven pan-tilt camera system was employed to simulate such rapid eye motion. Motions are so fast that motion blur, caused by relative motion between the camera and an object, would prevent any possibility of scene interpretation. Using knowledge of the dynamics and input controls, the paper proposes an approach to estimate the point spread function (PSF), which is a blur kernel that describes the camera motion during an exposure window. This PSF is then used to remove motion blur and detailed experimental results are presented.

In this paper Cupec, Nyarko, Filko, Kitanov and Petrović address the issue of localization, which they treat as a recognition problem on depth images. The authors explore the use of higher-level geometric primitives, such as 3D planar surfaces and line segments extracted from depth images for place recognition and probabilistic matching, along with the pose estimation. The use of structural geometric information makes the strategy robust with respect to lighting, occlusions and dynamic changes in the environment. The method is evaluated for indoor environments where the geometric primitives are readily extracted and efficiently matched with those in the previously constructed environment model.

The final paper in this special issue considers the problem of finding appropriate robotic grasps. Lenz, Lee and Saxena consider this as a detection problem: given noisy and partial views of objects from an RGB-D sensor viewing a scene containing multiple objects, what are the best locations to place the robot's gripper? It exploits the use of recently popularized deep learning techniques to learn effective features for grasp detection. The authors devote particular care to the design of the network architecture and run-time evaluation, so as to enable fast and robust detection and handling of multimodal data. The method is shown to outperform the previously proposed methods and results are presented for a Baxter robot grasping objects of a table top.

We are excited and encouraged by these latest results, which show great progress in the long-held dream of creating competent and capable robots that are guided and informed significantly by vision sensors, as we are. This special issue presents systems capable of working over large scales and long time periods in complex indoor and outdoor natural environments and with often adverse viewing conditions. We hope you enjoy this issue devoted to fostering links between research in robotics and vision. We look forward to seeing further contributions in this fascinating research area in future special issues on this topic.

Guest


Elodie Bouaziz

Marianne Canonico
email: marianne.canonico@inserm.fr

Céline Verstuyft

Laure Carcaillon

Frédéric Martin

Pierre-Yves Scarabin

Anne Guiochon-Mantel

Does the progesterone receptor genetic polymorphism +331G/A hPR influence the risk of venous thromboembolism among postmenopausal women using hormone therapy? The ESTHER Study

Keywords: progesterone receptor, venous thromboembolism, hormone therapy

Hormone therapy (HT) increases venous thromboembolism (VTE) risk among postmenopausal women. Data on the influence of steroids receptors polymorphisms on this association remain scarce. Since progesterone receptor (hPR) is expressed in human veins and specific progestogens increase VTE risk, we investigated the impact of the functional +331G/A hPR polymorphism on the association of VTE with HT. Using the data of the ESTHER study, we showed that ORs for VTE in current users of progesterone or progestins were not significantly different by hPR +331G/A genotype status. hPR polymorphism appears not to have a significant effect on VTE risk related to HT.

Introduction

Progesterone or progestins are systematically used in combination with estrogen among nonhysterectomised women who require hormone therapy (HT). Cardiovascular disease, including venous thromboembolism (VTE), is an important determinant of the benefit-to-risk profile of HT. Both observational studies and clinical trials have shown a significant increase in VTE risk among postmenopausal women using HT [START_REF] Canonico | Hormone replacement therapy and risk of venous thromboembolism in postmenopausal women: systematic review and meta-analysis[END_REF]. While the route of estrogen administration represents a major determinant of VTE risk among HT users, recent findings have suggested a differential impact of the progestogen subgroups on thrombotic risk [START_REF] Canonico | Hormone therapy and venous thromboembolism among postmenopausal women: impact of the route of estrogen administration and progestogens: the ESTHER study[END_REF].

Elevated risk among postmenopausal women using HT could be due to venous stasis caused by changes in venous distensibility and capacitance induced by progestogens. These effects can be mediated by human nuclear progesterone receptor (hPR) since it has been found in endothelial and smooth muscle cells of the vessel walls [START_REF] Perrot-Applanat | Progesterone receptor expression in human saphenous veins[END_REF]. hPR is a member of the steroidreceptor superfamily. Its gene uses two separate promoters to produce two isoforms, hPR-A and hPR-B. hPR-B, which presents an additional 164 aminoacids in its N-terminus domain, is a more potent transactivator than hPR-A. The balance hPR-B to hPR-A is involved in several pathophysiological actions of progesterone [START_REF] Mote | Clarke CL Ovelapping and distinct expression of progesterone receptors A and B in mouse uterus and mammary gland during the estrous cycle[END_REF]. One promoter region polymorphism, +331G/A hPR, creates a transcription start site. The +331A hPR produces a greater amount of hPR-B protein than +331G hPR in endometrial cell lines thus increasing the ratio hPR-B/hPR-A [START_REF] Vivo | A functional polymorphism in the promoter of the progesterone receptor gene associated with endometrial cancer risk[END_REF]. This functional polymorphism is associated with a risk of endometrial cancer [START_REF] Vivo | A functional polymorphism in the promoter of the progesterone receptor gene associated with endometrial cancer risk[END_REF]. Whether the balance hPR-B/hPR-A influences the association of VTE with progestogens remains unknown. Therefore, we used the data from the EStrogen and THromboEmbolism Risk (ESTHER) Study to investigate the impact of the +331G/A hPR polymorphism on the association between progestogens and VTE.

Subjects and methods

Subjects

The ESTHER study has been previously described [START_REF] Canonico | Hormone therapy and venous thromboembolism among postmenopausal women: impact of the route of estrogen administration and progestogens: the ESTHER study[END_REF]. Briefly, it was a multicenter case- hPR genotyping +331G/A hPR genotyping was performed on genomic DNA by a real-time polymerase chain reaction TaqMan allelic discrimination assay, designed using Primer Express Software (Applied Biosystems) with primers previously described [START_REF] Vivo | A functional polymorphism in the promoter of the progesterone receptor gene associated with endometrial cancer risk[END_REF]. Homozygous +331A/A hPR alleles were confirmed by genomic sequencing.

Statistical methods

To determine whether the controls were in Hardy-Weinberg equilibrium, their genotype frequencies were tested with the chi-square test. Crude odds ratios (OR) and 95% confidence intervals (CI) were estimated using an unconditional logistic regression. The original matching was taken into account by adjustment for age, center and admission date. Further adjustments included potential confounding variables. As previously described, current users of HT were compared with non-users in a joint model of both route of estrogen administration and progestogens types [START_REF] Canonico | Hormone therapy and venous thromboembolism among postmenopausal women: impact of the route of estrogen administration and progestogens: the ESTHER study[END_REF]. Interactions between HT and +331G/A hPR genetic status (either G/G or G/A+A/A) were tested by using a multiplicative OR model. Statistical analyses were performed using SAS statistical software (version 9.1, SAS Institute Inc, Cary, NC).

Results

A total of 195 DNA cases and 519 DNA controls were available and successfully genotyped for the +331G/A hPR polymorphism. The observed allele frequencies did not deviate from the expected Hardy-Weinberg distribution in our population and allele frequencies were in the same range to those described in Caucasian population (+331A hPR: 0.017, +331G hPR:

0.983).
The variant +331A hPR allele frequency was 6.2% and 4.8% among cases and controls respectively (OR=1.4; 95% CI: 0.9-2.5). Compared with non-users, OR of VTE in current users of oral and transdermal estrogens were 3.8 (1.5-9.9) and 1.1 (0.5-2.4) respectively among patients with +331G/G hPR genotype and 18.2 (0.4-923.6) and 0.8 (0.1-9.8) respectively among carriers of +331A hPR allele. Regarding the impact of progestogens, the OR of VTE in current users of micronised progesterone or pregnanes and norpregnanes were 0.8 (0.4-1.9) and 2.5 (1.1-6.3) respectively for women without the A allele and 0.3 (0.1-6.8) and 6.5 (0.3-166.2) respectively for carriers of the +331A hPR allele. Tests for interaction between HT and hPR status on VTE risk were not significant (Table 1). Furthermore, there was no interaction of +331G/A hPR genetic polymorphism with body-mass index (BMI) on VTE risk (data not shown).

Discussion

To our knowledge, this study is the first to investigate the impact of +331G/A hPR genetic polymorphism on VTE risk. The present analysis confirms that micronised progesterone and pregnanes are not associated with an increased VTE risk whereas norpregnanes may be thrombogenic. In addition, our results show that the +331G/A hPR polymorphism has no significant influence on VTE risk among postmenopausal women using HT.

Our results suggest that the +331G/A hPR polymorphism may be not functional in endothelial and smooth muscle cells of the vessel wall. Indeed, its effect has only been studied in vitro in endometrial cancer cells [START_REF] Vivo | A functional polymorphism in the promoter of the progesterone receptor gene associated with endometrial cancer risk[END_REF]. Alternatively, they suggest that there is no interaction between nuclear hPR and VTE and that the progestogens thrombotic effects are non genomic. At last, these effects may be mediated not only by hPR but also by other steroid receptors, such as androgen receptor.

As BMI is also a VTE risk factor and the interaction between BMI and the +331G/A hPR polymorphism has previously been described [START_REF] Vivo | A functional polymorphism in the promoter of the progesterone receptor gene associated with endometrial cancer risk[END_REF] we tested this interaction in our population.

Interestingly, there was no interaction of +331G/A hPR genetic polymorphism with BMI on VTE risk.

The validity of the ESTHER study has been discussed [START_REF] Canonico | Hormone therapy and venous thromboembolism among postmenopausal women: impact of the route of estrogen administration and progestogens: the ESTHER study[END_REF]. In this analysis, the main limitation is the low sample size of subgroups and the limited statistical power to detect an interaction between HT and hPR genetic status. Of particular interest is the population stratification in genetic investigations [START_REF] Canonico | Hormone therapy and venous thromboembolism among postmenopausal women: impact of the route of estrogen administration and progestogens: the ESTHER study[END_REF].

In conclusion, further studies are needed to investigate the influence of hPR polymorphism, as well as other steroid receptors, and non genomic effects in the association of VTE with HT. Test for interaction between oral estrogen use and A allele was not significant (p=0.46) Test for interaction between transdermal estrogen use and A allele was not significant (p=0.70) Test for interaction between micronised progesterone + pregnanes use and A allele was not significant (p=0.51) Test for interaction between norpregnanes use and A allele was not significant (p=0.63)

  control study of VTE performed in France between 1998 and 2006. The population consisted of postmenopausal women aged 45 to 70 years with neither a personal history of VTE nor contra-indication to HT, nor predisposing factors for VTE. Cases with a first documented episode of idiopathic VTE (n=271) were matched to one to three controls (n=610) for age, center and admission date. The protocol was approved by INSERM and the local ethics committee. Written and informed consent was obtained from all women.

Table 1 :

 1 Odds ratio for VTE in relation to HT by genotype status of hPR +331G/AUsers of oral estrogen combined with nortestosterone derivatives were excluded and OR for VTE were estimated separately using the non-users as the reference group (7 cases and 6 controls among patients with hPR +331G/G genotype [OR=4.0; 95% CI: 1.3-12.6] and 1 case and 0 control among carriers of hPR +331A allele [OR not available]).

	hPR status Hormone therapy	Cases (n=187) Controls (n=513) Odds ratios (95% CI)*
	GG	Non-use	88	292	1
		Oral estrogen	30	32	3.8 (1.5-9.9)
		Transdermal estrogen	46	142	1.1 (0.5-2.4)
		No progestogen	11	33	-
		Micronised progesterone + Pregnanes 41	110	0.8 (0.4-1.9)
		Norpregnanes	24	31	2.5 (1.1-6.3)
	GA or AA	Non-use	13	27	1
		Oral estrogen	5	2	18.2 (0.4-923.6)
		Transdermal estrogen	5	18	0.8 (0.1-9.8)
		No progestogen	1	3	-
		Micronised progesterone + Pregnanes 4	14	0.3 (0.1-6.8)
		Norpregnanes	5	3	6.5 (0.3-166.2)

* Adjusted for age, center, admission date, family history of VTE, history of varicose veins and obesity

Acknowledgements:

We are grateful to L. Dubert for her precious technical assistance and Pr. P. Jaillon from the department of Pharmacology of St Antoine Hospital, University Paris 6.

Funding/Support

The study was supported by the Fondation de France, by the Fondation pour la Recherche Médicale (FRM) and Institut National de la Santé et de la Recherche Médicale (Inserm), and by grants from Aventis, Besins International, Sanofi, and Servier Institute.

Funding/Support

The study was supported by the Fondation de France, by the Fondation pour la Recherche Médicale (FRM) and Institut National de la Santé et de la Recherche Médicale (Inserm), and by grants from Aventis, Besins International, Sanofi, and Servier Institute.

Role of sponsors

The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Conflict of interest: no.

Coordinating center: Inserm U780 (G Plu-Bureau, E Oger, L Carcaillon, M Canonico and PY Scarabin).

Conflict of interest: no.