Kata Gábor

Marianna Apidianaki
email: marianna@limsi.fr

Benoît Sagot
email: benoit.sagot@inria.fr

Éric Villemonte De

La Clergerie
email: eric.de_la_clergerie@inria.fr

Boosting the Coverage of a Semantic Lexicon by Automatically Extracted Event Nominalizations

Keywords: lexical acquisition, nominalization, WordNet

An important trend in recent works on lexical semantics has been the development of learning methods capable of extracting semantic information from text corpora. The majority of these methods are based on the distributional hypothesis of meaning and acquire semantic information by identifying distributional patterns in texts. In this article, we present a distributional analysis method for extracting nominalization relations from monolingual corpora. The acquisition method makes use of distributional and morphological information to select nominalization candidates. We explain how the learning is performed on a dependency annotated corpus and describe the nominalization results. Furthermore, we show how these results served to enrich an existing lexical resource, the WOLF (Wordnet Libre du Franc ¸ais). We present the techniques that we developed in order to integrate the new information into WOLF, based on both its structure and content. Finally, we evaluate the validity of the automatically obtained information and the correctness of its integration into the semantic resource. The method proved to be useful for boosting the coverage of WOLF and presents the advantage of filling verbal synsets, which are particularly difficult to handle due to the high level of verbal polysemy.

Introduction

Distributional similarity has a prominent role in lexical semantics in recent years: several methods have been proposed to exploit the distributional hypothesis [START_REF] Harris | Distributional structure[END_REF] and infer semantic information by identifying distributional patterns in texts. These methods offer an alternative to the manual creation of lexical resources for various applications. The majority of the work carried out in this domain aims at extracting lexical semantic properties from monoor multilingual corpora. Word sense induction methods generally use unsupervised clustering techniques based on contextual similarity to identify the senses of words in texts [START_REF] Pantel | Discovering word senses from text[END_REF][START_REF] Véronis | Hyperlex: lexical cartography for information retrieval[END_REF][START_REF] Agirre | Semeval-2007 task 02: Evaluating word sense induction and discrimination systems[END_REF][START_REF] Apidianaki | A Quantitative Evaluation of Global Word Sense Induction[END_REF][START_REF] Messiant | Acquisition de connaissances lexicales à partir de corpus : la sous-catégorisation verbale en franc ¸ais[END_REF]. However, other types of semantic information, such as hypernymy, hyponymy and other semantic relations, can also be extracted from corpora [START_REF] Hearst | Automatic acquisition of hyponyms from large text corpora[END_REF]. Relations between words belonging to different parts of speech (PoS) categories can equally be identified, e.g. derivational relations between verbs and nouns or adjectives and adverbs [START_REF] Fabre | Extraction de relations sémantiques entre noms et verbes au-delà des liens morphologiques[END_REF]. This paper presents an unsupervised lexical acquisition experiment aimed at enriching a French lexical semantic resource with event nominalizations. The acquisition method makes use of distributional and morphological information to select nominalization candidates. The candidates are then used to enrich a lexical semantic resource, the WOLF (Wordnet Libre du Franc ¸ais [START_REF] Sagot | Combining Multiple Resources to Build Reliable Wordnets[END_REF]), created on the basis of Princeton WordNet (Fellbaum, 1998). Two complementary methods for adding new words to existing synsets will be presented, both of them exploiting the semantic relations present in WOLF. The extraction method proved to be useful for boosting the coverage of WOLF and has the capacity to fill verbal synsets, which are particularly difficult to handle due to the high level of verbal polysemy. Morpho-semantic derivational links between adjectives and adverbs have already been explored for the purpose of improving the coverage of WOLF [START_REF] Sagot | Extension et couplage de ressources syntaxiques et sémantiques sur les adverbes[END_REF]. Our work differs from earlier experiments with respect to the nature of derivational links to be identified: we do not limit our investigation to morphologically related word pairs. Another important difference is that we acquire lexical information from corpora using distributional methods instead of relying on existing lexical resources.

Comparison to related resources

Two French nominalization lexica are currently available: VERBACTION [START_REF] Tanguy | Webaffix : un outil d'acquisition morphologique dérivationnelle à partir du Web[END_REF] and Jeux de Mots [START_REF] Lafourcade | Détermination des sens d'usage dans un réseau lexical construit grâce à un jeu en ligne[END_REF]. VERBACTION contains verb-nominalization pairs where the noun is produced from the verb by morphological derivation and refers to the action expressed by the verb. Nominalization candidates were extracted from lexical resources and the Internet, using the method described in [START_REF] Hathout | An Experimental Constructional Database: The MorTAL Project[END_REF]. In its current state, the lexicon contains 9393 verb-noun pairs. Jeux de Mots is a relational database of lexical entries collected through an online word game1 . It contains a variety of lexical relation types (e.g. associated term, part of, synonymy, location, characteristic). In the interactive game, a lexical item and a relation type are shown to two users in parallel: they are asked to suggest words related to the source word by the displayed relation type. Relations suggested by both users are saved and asked to be validated by other users before being included in the lexical network. The database is constantly growing; at present, it contains over 1 300 000 relation instances among which 6 752 are considered as finalized (suggested at least 25 times). Our acquisition method presents the following advantages over currently existing resources:

• unlike VERBACTION, it is not limited to verb-noun pairs produced by morphological derivation (p.ex. : tomber, ('to fall') -chute, 'fall'; rouler, 'to travel/to drive' -circulation, 'traffic'),

• the method guarantees that verb-noun pairs are only included if they denote events,

• it is adaptable to specific domains and new corpora,

• finally, it provides a set of distributional contexts allowing to disambiguate between different senses and to position the candidates in a lexical semantic resource.

The extraction of nominalization candidates is done in three steps:

• First, semantic similarity is calculated on the basis of syntactic distribution.

• Second, a morphological module checks whether there is a morphologically related noun among the highest ranking candidates.

• Finally, the so-called 'event indicator' score is calculated to filter out verb-noun couples that do not refer to events.

Acquisition of Nominalization Candidates

Event nominalizations have a special interest for language processing: they are characterized by a complex lexical structure as they often preserve the (partial or complete) subcategorization pattern of the verb they are derived from, or are semantically related to. Lexical information related to the argument structure of nouns can be exploited to improve parsing performance with respect to attachment ambiguities. Moreover, this kind of lexical information can be useful in NLP applications as diverse as information extraction or semantic role labeling (SRL). Current works on nominalization acquisition are sparse and mostly concentrate on enriching syntactic and semantic lexica or improving semantic role labeling [START_REF] Padó | Semantic role assignment for event nominalisations by leveraging verbal data[END_REF][START_REF] Lapata | The disambiguation of nominalisations[END_REF]. Annotating the argument structure of nouns with semantic roles has actually received significantly less attention compared to verbal subcategorization. As demonstrated by [START_REF] Padó | Semantic role assignment for event nominalisations by leveraging verbal data[END_REF], nominal SRL is a more challenging task compared to verbal SRL, partly due to lack of data (both in terms of available lexical resources and annotated learning corpora). Nevertheless, the main reasons for the lower performance of nominal SRL are the task-specific difficulties related to verb-noun derivations: not all verbs refer to actions; not all action verbs can be nominalized; some deverbal nouns are lexicalized with a meaning which does not correspond to the event denoted by the verb. The mapping between the arguments of verbs and their nominalized equivalents is not always straightforward and can be ambiguous. Moreover, morphologically related nominalizations are sometimes overridden by lexicalized forms blocking the use of the derived word form. Our methodology aims at identifying nominalization equivalences even in cases where there is no direct morphological link between the verb and the noun. Therefore, we talk about word pairs related by morphosemantic derivation.

Three types of nominalizations can be distinguished:

• action nominalizations,

• result nominalizations,

• nominalizations corresponding to a participantmost typically to the agent -of the base verb.

Result nominalizations do not correspond to an event , consequently, they usually do not preserve the argument structure of the base verb. On the other hand, nominalizations corresponding to the action itself or to one of its participants can inherit the complete subcategorization frame of the verb or a subset of it, and display it as syntactic dependents. In the present study, we are interested in action nominalizations and we hypothesize that it is possible to identify them by exploiting the distributional similarity between verbal and nominal complement structures observed in a corpus.

Corpus

The distributional similarities between nouns and verbs were calculated using information extracted from a syntactically analyzed corpus (Table 1). The corpus contains around 700 million words covering diverse topic areas and was parsed with the TAG FRMG parser with two output formats: the Passage format (used in various French evaluation campaigns) and the DepXML format. Verbal and nominal distributions were both represented as dependency triplet frequencies. A dependency triplet is composed of a pair of lemmata with a labelled dependency relation between them ( 2). The distribution of nouns was extracted from the Passage format of the parser output [START_REF] Vilnat | PASSAGE Syntactic Representation: a Minimal Common Ground for Evaluation[END_REF], which includes chunking and dependency information. Subcategorization frames for verbs were extracted from the DepXML format (Villemonte de la Clergerie, 2010). A full subcategorization frame indicates the complementation pattern of the verb (subject, object, oblique complements, adjuncts) together with the lemma of the head word occurring in the given position.

Distributional Similarity

The representation of verbs and nouns was constructed from the dependency triplets extracted from the parsed corpus. The reason for preferring individual dependency relations over complete subcategorization frames is twofold. First, using complete subcategorization frames could lead to a data sparsness problem; second, nouns usually do not display more than one or two of their arguments in a sentence, which makes it difficult to establish a correspondance with complete subcategorization frames. When constructing the representation, a general filtering of dependency triplets was carried out to select the dependency relation types relevant for each verbal entry. All subcategorization pattern occurrences were extracted from the corpus and a list of generalized frames was created by deleting all lexical information from the patterns. Subsequently, the Pointwise Mutual Information P M I(x y) was calculated for each pair of verb+subcategorization frame (x, y) in the corpus:

P M I(x y) = log p (x, y) p (x) p (y) , (1)
where the probabilities p (y), p (x) and p (x, y) were calculated by maximum likelihood estimate from the corpus. Verb-specific frame lists were then obtained by selecting the subcategorization frames with a PMI value over a threshold established during test runs. Finally, dependency relation types were extracted from these frames, yielding a list of syntactic relations characteristic of a given verb to be compared with nominal distributions. Relations included in verbal representations were not limited to arguments: adjunct functions were also considered provided that they were characteristic of the lexical entry.

Given the list of lemma-specific dependency relation types, the frequencies of relevant dependency triplets instances were included in verbal representations. No filtering was however applied when constructing nominal representations and all dependency relation types were taken into account. Dependency triplet instances were characterized by the type of the relation, the lemma and the morphological description of the complement. The only exception were verbal complements, in which case the lexical information was replaced by the PoS category (infinitive or clause), as we consider that verbs and nouns accepting sentential arguments do not make semantic restrictions on these types of complements.

To calculate a distributional similarity from the obtained syntactic representation, an algorithm capable of establishing a mapping between verbal and nominal argument structures was needed. We observe that verbs and nouns do not realize their arguments in the same way and that some syntactic forms can be ambiguous with respect to the grammatical function:

VERB NOUN subject/passive par de/par object/passive sujet de avec entre (e.g. discuter, discussion) de/à sur (e.g. réfléchir,réflexion)

The mapping between verbal and nominal dependency relations was carried out as follows. Most oblique (prepositional) complements of the verb were mapped directly to their nominal counterparts (e.g. voter/vote au parlement, au conseil). We resorted to heuristics to disambiguate the preposition 'de' which, when used with event nominalizations, can correspond to the subject, the direct object or an oblique complement of the base verb with the same preposition.

In order to decide whether a given verb is transitive or not, we considered the proportion of transitive subcategorization frames over the occurrences of the verb in the parsed corpus. The verb was considered as transitive if it had at least one transitive subcategorization frame (or a frame which had the verb in passive) with a PMI value above the previously defined treshold. The preposition 'de' was mapped to the direct object function of transitive verbs, and to the subject function of intransitive verbs. In the case of intransitive verbs, the subject of nominal candidates was used as a meta-function including both complements with the preposition 'de' and complements with 'par'. On the other hand, the subject function was not considered at all for transitive verbs.

We used the Dice index to calculate a distributional similarity in terms of the intersection of the lemmata occurring in the same or equivalent syntactic positions:

S D (x y) = 2 X Y X Y (2)
To compensate the bias introduced by the generalization of complements belonging to a verbal category, a weight that was 20 times higher was given to complements with a specified lemma as opposed to complements characterized by PoS category. A minimum similarity threshold was set to rule out irrelevant candidates. For each verb, the highest ranking candidates were kept for further processing. The following modules re-rank or filter these candidates.

Morphological Similarity

The morphological similarity module detects whether there is a candidate which is likely to be produced from the verb by morphological derivation. It is important to note that this information is only taken into account if the noun is already a potential candidate, i.e. if it has a similar distribution. Morphological similarity is calculated using the edit distance and a manually constructed list of derivational suffixes. The distance is calculated between the verb stem (after deleting the infinitival suffix) and possible nominal stems, including the complete form as well as any stem obtained after matching the list of potential nominalization suffixes. The two words are considered as morphologically related if the following criteria were satisfied for at least one of the potential nominal stems:

• the edit distance is lower than 3

• the length of the verb minus the edit distance is greater than 3.

Moreover, the two forms were considered as related independently of the length of the verb if the verb and the noun only differed in the infinitival suffix.

Filtering by the Event Indicator

The third module used assigns a value to individual nominal candidates , indicating the likeliness that a noun denotes an event. This metric is equally calculated from syntactic distributions.

A recurrent type of irrelevant nominalization candidates concerns nouns exhibiting a strong distributional similarity with a verb but actually not denoting events. This occurs when a noun is accidentally characterized by the same contexts without having the same semantic relation to the lemma/lemmata in the context (e.g. trembler, subject: terre 'the earth shakes' and pomme de terre 'potato', 'earth apple'). More importantly, some nouns can have a systematically high distributional similarity with a big number of verbs due to the fact that they accept a variety of syntactic complements. Typical examples are nouns denoting quantities or behaving like determiners (e.g. unité, kilogramme, totalité). Finally, participant nominalizations are also likely to accept a subset of the arguments of the base verb but since they do not refer to events, they were excluded from the list of candidates. Instead of constructing a stop-word list to filter out these entries, a dynamic filtering module was used that rules out both types of false nominalizations.

The filtering is based on a value that indicates the likeliness that the noun corresponds to an event. Similarly to [START_REF] Arnulphy | Les entités nommées événement et les verbes de causeconséquence[END_REF] who aim at extracting named entities denoting events, we relied on syntactic contexts specific to events. However, unlike in their experiment, we did not dispose of manually annotated corpora. We therefore adopted the following semi-automatical process. We defined a metric called 'event indicator', based on the hypothesis that event nominalizations are likely to occur in argument positions with verbs that semantically select an event as their argument. This event is syntactically realized either as an event nominalization or as a complement with a verbal category (clause or infinitive). Consequently, we extracted from our corpus a list of verbs subcategorizing for infinitives or clauses.

The resulting list was manually validated. We also indicated the syntactic realization that the verb required for its nominal candidate wherever it was different from the syntactic realization of the clausal/infinitival complement (e.g. accuser d'avoir volé -accuser de vol with the same syntactic function, vs refuser de signer -refuser la signature with different syntactic functions). For every nominalization candidate, we calculated the proportion of its occurrences in these event-like syntactic contexts, compared to the total number of its occurrences. This number was then weighted by the number of different types of event-like contexts characterizing the noun. The threshold was defined during test runs; we opted for a value that allowed to reduce the number of candidates by 25%.

Results

The method described above was applied to our corpus to detect nominalizations for 3 351 verbs with at least 50 occurrences in the corpus. 2 424 verbs were assigned at least one nominalization; the rest of the verbs did not have any nominalization candidates for one of the following two reasons:

• the quantity of data was not sufficient to extract verbspecific subcategorization frames and create a representation of the verb's syntactic distribution,

• the verb does not denote an event (every potential candidate was filtered out by the event indicator metric).

Subsequently, we defined a reliability threshold in terms of distributional similarity. This additional step serves to eliminate further candidates of verb-noun pairs that do not denote an event. Following this filtering phase, 1 136 verbs were assigned at least one candidate. Results are presented for manual evaluation in the form of ticketsincluding a suggested nominalization, the strength of the distributional similarity as a reliability metric, and a few examples of shared contexts (dependency relations and typical lexical items occurring in the dependent position) illustrating the meaning in which the verb and the noun are supposed to be related. Figure 1 illustrates nominalization tickets with the relevant syntactic contexts shared between the verb and the noun.

Among the final list of candidates, we find verb-noun couples produced by morphological derivation (e.g. nommer,

'to appoint' -nomination, 'appointment' or protéger, 'to protect' -protection), but also semantically related words without any morphological relation (e.g. fausser, 'to distort' -distorsion; redouter, 'to fear' -crainte, 'fear'). If the list contains one or more morphologically related terms, these are presented as unique candidates; otherwise, the first two candidates (ranked by distributional similarity) are presented in the ticket.

A manual evaluation was performed on 161 randomly selected tickets, among which 113 (70%) were considered as correct nominalizations. A subsequent analysis allowed to identify recurrent errors among the nominalizations suggested by our algorithm. The most frequent source of erroneous candidates was the generalization of infinitival or clausal complements in the syntactic representation: the generalization was not sufficiently compensated by the lower weight assigned to these complements and hence yielded biased representations. On the other hand, the generalization was necessary in order to avoid sparseness data problems for these verbs.

As the algorithm uses data from an automatically annotated corpus, some of the resulting errors are due to noisy input data, and in particular to wrong lemmatization in the corpus. For instance, the method suggests to connect the word fil ('thread', 'wire') to the verb pendre ('to hang'). A closer look at the contexts presented in the ticket showed that the verb 'pendre' was often mistaken in the corpus for being the lemma of the form pendant ('during', preposition), which corresponds to the meaning of 'fil' in the multiword expression 'au fil de'.

It is well known that distributional methods have difficulties distinguishing words in a synonymy relation from antonyms, both relations being characterized by similar syntactic contexts. We encountered the same problem for some of our nominalization candidates (e.g. fermer, 'to close' -ouverture, 'opening').

In other cases, we obtain correct nominalization candidates but a subset of the nominal and verbal syntactic contexts cannot be mapped to each other due to different semantic roles: the two words present the same event from a different perspective (e.g. acheter, 'to buy' -vente, 'selling').

Inversely we find examples of exact nominalization with respect to the suggested semantic role, which does not refer to the same event as the verb (e.g. enlever, 'to kidnap' -disparition, 'disappearance'; tuer, 'to kill' -mort, 'death'). Finally, a set of candidates contain semantically related words which do not designate an event: peser, 'to weigh' -poids, 'weight'; remonter, 'to go/date back to'origine, 'origin'.

Enriching the Lexical Resource

WOLF

The nominalization candidates were used to enrich WOLF with new lexical entries. WOLF (WOrdnet Libre du Franc ¸ais, Free French Wordnet) is a freely available semantic lexical resource for French [START_REF] Sagot | Combining Multiple Resources to Build Reliable Wordnets[END_REF] net, WOLF is a lexical database in which words (lexemes, literals) are divided by parts of speech and organized into a hierarchy of nodes. Each node has a unique id, and represents a synset . A synset groups one or more synonymous words that denote the same concept. WOLF was built automatically from the PWN 2.0 and various multilingual resources, using two complementary approaches. Polysemous lexemes were dealt with using an approach that relies on word-aligned parallel corpora in five languages, including French. Several multilingual lexica were extracted from these aligned corpora. The obtained lexica were semantically disambiguated using the wordnets of the corresponding languages. Monosemous PWN lexemes were translated by using bilingual lexica extracted from wiki resources (Wikipedia, Wiktionary) and thesauri. WOLF contains all PWN 2.0 synsets, including those for which no French lexeme is known. The latest version of WOLF includes 32 351 non-empty French synsets and 38 001 literals, and hence provides better coverage than the manually built French part of EuroWordNet [START_REF] Vossen | EuroWordNet : a multilingual database with lexical semantic networks for European Languages[END_REF] or the automatically constructed JAWS (Juste Another WordNet Synset) [START_REF] Mouton | JAWS: Just Another WordNet Subset[END_REF]. However, the coverage of WOLF is still limited compared to PWN (115 424 synsets for 145 627 literals). The work presented in the following sections is aimed at increasing the coverage of WOLF by automatically assigning lexical entries to verbal synsets.

Heuristic Method

New entries can be added to WOLF either by filling empty synsets or by adding synonyms to non-empty synsets. As a first attempt, we started filling up the verbal synsets. Incidentally, they are the most difficult to handle due to the high level of polysemy that characterizes many verbs. The synsets to be filled are selected by exploiting the derivational links already present in WOLF (coming from the structure of the Princeton WordNet). For each pair of a verb and a nominalization candidate, if the noun figured in some WOLF synset(s), we extracted the verbal synsets linked to it (them) by a derivational relation. We obtained candidates of the form {V,S}, meaning that verb V is suggested to be added to synset S. 2 353 candidates were created in this way.

The first approach used to add verbs to empty synsets was

Semantic Similarity

The nominalization candidates for which this heuristic approach was not applicable were the ones that had more than one empty verbal synsets. These candidates were integrated into WOLF by a disambiguation method based on distributional similarity. Given a candidate {V,S} the adequacy of verb V for filling synset S was measured by combining two criteria.

The first criterion concerns the reliability of the nominalization itself, i.e. the distributional similarity between the noun and the verb: nominweight(v,s). 2 The second criterion concerns the semantic similarity between the verb and the synset.

For calculating the semantic similarity between a verb and a synset S, we represented each synset by a bag of words containing the words present in S and in the other synsets linked to S by at most three steps of distance in terms of hypernymy or hyponymy relations. The similarity of a verb to a synset (semweight(v,s)) was calculated by comparing the information acquired for the verb from the learning corpus and the bag of words characterizing the synset: the lowest figure among the similarity measures between the verb and each word from the bag of words representing the synset S was taken into account.

In order to make best use of these two metrics which we presume to correlate with the semantic closeness of the verb to the synset, we tried to find a way to combine them. We decided to use the MegaM classifier 3 , a maximum entropy based algorithm, to associate a weight to the two features.

2 Distributional similarities between nouns and nouns, as well as between nouns and verbs, were calculated on the same corpus using the data and the metrics described above.

3 http://www.cs.utah.edu/ hal/megam/ Learning data was created from the {V,S} candidates which are known to be correct because the verb already figured in the proposed synset in WOLF. Negative examples were also constructed assuming, for the sake of the experiment, that if the synset is not empty but does not contain the verb proposed by our method, the proposition is incorrect. (This is obviously an approximation, since we were interested in finding out which of them were correct). The classifier was trained on this data with the features semweight, nomweight and their product; it assigned a score weight(v,s) to each of the 1 716 candidates.

Evaluation

We proceeded to a manual evaluation of the resulting {V,S} candidates, to verify whether the assignment of the verb to the synset was correct. Only candidates coming from correct nominalizations were considered at this phase, since the precision of nominalizations had already been evaluated (cf. section 3.5.). First, we examined 63 randomly chosen synsets filled by the heuristic method. The errors due to incorrect nominalization candidates put aside, this method positions 95% of the candidates in the correct synset. Second, we selected 93 candidates (coming from a correct nominalization), assigned by the disambiguation method.

Due to the higher level of ambiguity in the input data (i.e. more than one empty synsets for each lemma), a lower precision can be expected for this task. As a baseline, we calculated the proportion of correct {V,S} assignments in the totality of {V,S} couples generated from derivational relations: this represented a precision of 63%. The efficiency of the disambiguation method can be perceived as its capacity to reduce this error rate by ranking candidates according to their weight score. The score proved to correlate with the precision of the assignment: 80% of the 25% highest ranked of candidates were correct, while the proportion drops to 69% when considering the top 50% highest-ranked candidates.

Conclusion

We presented an unsupervised method for extracting information about event nominalizations from monolingual corpora. The results of the method were exploited to improve the coverage of a lexical semantic resource, the WOLF. The proposed algorithm is based on distributional analysis: a set of verb-noun couples, with similar syntactic contexts and semantic role assignment, is extracted from our learning corpus. The distributional analysis is complemented by a morphological module and a filtering on the basis of an 'event indicator' measure.

The main advantage of our method over currently existing nominalization lexica is to be able to extract morphologically unrelated nominalizations, and to provide a set of contexts indicating the meaning in which the noun and verb are related and thus facilitate manual validation. Two complementary methods were employed to integrate the nominalization candidates into WOLF. Prior to this, we used the derivational links present in the structure of WOLF to create {lemma, synset} candidates to be added to the resource. A heuristic approach was used to add new lemmata to verbal synsets. The method positioned a significant number of candidates into WOLF with high precision. A disambiguation method completes the procedure by dealing with candidates for which the heuristic method did not apply. This method assigns an adequacy score to each {lemma, synset} pair, and the synset in the highest ranked pair is filled by the verb. The manual evaluation showed that this measure correlates with the actual correctness of the candidate. Although the current results do not allow a fully automatic population of WOLF, the candidates resulting from our method considerably facilitate the manual extension of the lexical resource.

Figure 1 :

 1 Figure 1: Nominalization candidates with contexts.

Table 1 :

 1 Table 2 lists some of the syntactic contexts extracted for the noun inauguration and the verb inaugurer. Composition of the corpus

	Corpus	#words	info
	Wikipedia (fr)	178.9110M encyclopedy
	Wikisource (fr)	63.9771M literature
	EstRepublicain 144.8779M press
	JRC	66.5447M EU directives
	EP	41.5288M parliamentary debates
	AFP	248.3240M 400K news wires
	TOTAL	744.1638M

Table 2 :

 2 Dependency triplets and subcategorization frames for inauguration and inaugurer

Table 3 :

 3 Top 20 candidats as ranked by the method based on semantic similarity. 17 of them are corrects, while the three erroneous canidates are coming from a false lemmatization of reflexive verbs: se nettoyer would be correctly positioned in the given synsets with a reflexive lemma and se propager is semantically very close to the suggested synset. Erroneous candidates are italicized. a heuristic one: if only one empty synset existed among the synsets proposed for a verb, the verb was added to this synset. This method allowed us to fill 377 synsets (among which 45 were filled with more than one verb), triggered by 530 nominalization candidates already present in WOLF.

http://www.jeuxdemots.org/

Acknowledgements

The work reported in this paper was accomplished within the project SCRIBO, funded by SYSTEM@TIC, and within the project ANR EDyLex (ANR-09-CORD-008).


Benoît Sagot
email: benoit.sagot@inria.fr

Danlos Laurence
email: laurence.danlos@linguist.jussieu.fr

Alpage

Merging syntactic lexica: the case for French verbs

Keywords: Lexicon Merging, Syntactic Lexicons, Lefff, Lexicon-Grammar, Dicovalence, LVF

Syntactic lexicons, which associate each lexical entry with information such as valency, are crucial for several natural language processing tasks, such as parsing. However, because they contain a rich and complex information, they are very costly to develop. In this paper, we show how syntactic lexical resources can be merged, in order to take benefit from their respective strong points, and despite the disparities in the way they represent syntactic lexical information. We illustrate our methodology with the example of French verbs. We describe four large-coverage syntactic lexicons for this language, among which the Lefff , and show how we were able, using our merging algorithm, to extend and improve the Lefff .

Introduction

Syntactic lexicons are crucial for several natural language processing tasks, such as parsing, be it symbolic [START_REF] Riezler | Parsing the Wall Street Journal using a Lexical-Functional Grammar and discriminative estimation techniques[END_REF][START_REF] Franc | Comment obtenir plus des méta-grammaires[END_REF] or even statistical [START_REF] Collins | Three generative, lexicalised models for statistical parsing[END_REF][START_REF] Versley | Scalable discriminative parsing for German[END_REF]. Syntactic lexicons are rich and complex resources, and their development is a costly task. Although a lot of work has been published on the automatic acquisition of syntactic lexica, the resources that have a coverage and an accuracy large enough for being used as linguistic descriptions, e.g., in symbolic parsers, have been developed manually or semi-automatically, sometimes for several decades. In this paper, we focus our study on French verbs. There exist today four large-coverage syntactic lexical resources for French, that provide information about the valency of lexical entries, i.e., subcategorization frames and other syntactic information relevant for describing the syntactic behaviour of predicative lexical units. These resources are Lexicon-Grammar tables [START_REF] Gross | Méthodes en syntaxe : Régimes des constructions complétives[END_REF]Boons et al., 1976b;Boons et al., 1976a;[START_REF] Guillet | La structure des phrases simples en franc ¸ais : Les constructions transitives locatives[END_REF], the verb valency lexicon Dicovalence (van den [START_REF] Van Den Eynde | Valency dictionary -DICOVALENCY: user's guide[END_REF], the verbal syntactico-semantic lexicon LVF [START_REF] Dubois | Les verbes franc ¸ais[END_REF], and the Lefff [START_REF] Sagot | The Lefff 2 syntactic lexicon for French: architecture, acquisition, use[END_REF][START_REF] Sagot | The Lefff , a freely available, accurate and large-coverage lexicon for French[END_REF]. All these resources use both syntactic and semantic criteria for defining either one or several entries for the same verb lemma. Therefore, these lexicons can be considered as an inventory of lexemes (as opposed to verb lemmas) associated with syntactic information. The objective of this paper is to show how these diverse resources can be leveraged for improving one of them, the Lefff , by developing and applying merging techniques for valency lexicons. In this paper, we limit ourselves to verbal entries, for at least two reasons. First, they are best covered in terms of syntactic information than other categories. For example, LVF and Dicovalence only cover verbs. Second, verb valency is crucial in the first NLP application of syntactic lexicons, namely parsing systems. Merging syntactic lexicons is not a straightforward task. Indeed, there is no real consensus on the way syntactic information should be modeled and formalized. There are discrepancies among resources, which differ in various ways:

• coverage: for example, Dicovalence has focused on reasonably frequent entries of fairly frequent verbal lemmas, wheras LVF has tried to have as large a coverage as possible;

• level of granularity of the set of entries for a given lemma (i.e., level of granularity used for distinguishing lexemes from one another): for example, LVF can distinguish two entries which differ only at a very finegrained semantic level, whereas other resources will contain only one corresponding entry (see examples below);

• nature and level of granularity of the syntactic properties they describe: for example, Lexicon-Grammar tables include a large amount of non-standard syntactic information (e.g., symetric verbs), but does not really cover reflexive and reciprocal realizations using the pronoun se, whereas Dicovalence only describes pronominal realizations of syntactic functions which include the reciprocal and reflexive se realizations,

• level of formalization: Dicovalence and the Lefff are immediately usable in NLP tools, contrarily to LVF or Lexicon-Grammar tables;

• definition of what is considered syntactic argument as opposed to an adjunct: Dicovalence considers as arguments complements that other resources sometimes consider as adjuncts.

The methodology we have developed for merging syntactic lexicons has been developed in the last years [START_REF] Sagot | Méthodologie lexicographique de constitution d'un lexique syntaxique de référence pour le franc ¸ais[END_REF][START_REF] Sagot | Description et analyse des verbes désadjectivaux et dénominaux en -ifier etiser[END_REF][START_REF] Sagot | Constructions pronominales dans dicovalence et le lexique-grammaire -intégration dans le lefff[END_REF][START_REF] Ángel Molinero | A morphological and syntactic wide-coverage lexicon for spanish: The Leff e[END_REF]. Other teams have worked on this task, such as [START_REF] Crouch | Unifying Lexical Resources[END_REF] for English and Necs ¸ulescu et al. ( 2011) for Spanish. They address in different ways the issue of mapping lexical entries for a given lemma from various input lexicons to the one another, although these entries might have been defined at least in part using semantic criteria. In the work by [START_REF] Crouch | Unifying Lexical Resources[END_REF], the authors rely on the fact that, in (some of) their input lexicons (VerbNet and Cyc), lexical entries, which correspond to lexemes, are associated with WordNet synsets. This allows them to put together lexical entries that are associated with identical or related senses, although they resort to non-trivial techniques for dealing with various types of discrepancies and inconsistencies.

On the other hand, Necs ¸ulescu et al. ( 2011) simply want to merge subcategorization lexicons, i.e., lexicons that list all possible subcategorization frames for a given verb lemma (as opposed to lexeme). With their strategy, they avoid the need for correctly mapping to the one another lexical entries that are defined based on syntactico-semantic criteria. However, the resulting lexicon is then only a subcategorization lexicon, and not a full-featured syntactic lexicon associating syntactic information with each lexeme.

In our case, our input resources for French verbs do not contain WordNet synset information. Nevertheless, we do want to take advantage of sense distinctions between entries, and to produce a merged lexicon at the lexeme level, that preserves these sense distinctions to the appropriate extent. Our methdology can be sketched as follows. First, we chose a model for representing syntactic information, and convert all input resources in this model, after a careful linguistic analysis. In this paper, this common model is Alexina, the lexical framework on which the Lefff is based. This is because Alexina lexicons, as mentioned above, are immediately usable in NLP tools. Moreover, and contrarily to our other input lexicons, the Lefff strongly relies on the notion of syntactic function, which is the basis for many parsing systems. In a second step, we try and create groupings, i.e., sets of lexical entries possibly extracted from more than one input resources and that will be merged in one entry in the output lexicon. Finally, we perform the actual merging. Such a methodology is useful for various reasons. Of course, it helps developing a resource that has a higher coverage and accuracy than all input resources, although some information might be lost during the conversion process. Second, it allows for an efficient manual work on the output resource, if such a work is considered; for example, pieces of information that originate in only one of the input resources are more dubious than others. Finally, as a consequence, it allows for detecting errors in the input lexicons, as will shall see below.

After a brief description of our four input syntactic lexicons in Section 2. illustrated with a running example, we describe in more details our merging methodology and algorithm (Section 3.). Then, we describe a set of experiments conducted in the last years that are based on this methodology (Section 4.). Finally, we draw several conclusions and indicate the next steps for this work. 1

1 If the paper is accepted, we will report on results we have obtained while trying to evaluate various syntactic lexicons by comparing the results of one of the best performing symbolic parsers for French when it uses one of these lexicons or another. These results are not included in this submision for space reasons.

Input resources

We shall not provide a detailed description of our input resources. Such descriptions can be found in the various publications related to each resource (see citations below). Rather, we shall illustrate these resources on a running example, the lemma vérifier 'check', 'verify'. In the reminder of this paper, we refer to the entry with id n for the lemma v in the resource i as v i n . For example, the (only) entry in the Lefff for the lemma vérifier is vérifier Lefff 1 . For simplification purposes, we use v i n both for the lexical entry in its original form and after its conversion in Alexina.

The Lefff and the Alexina lexical formalism

The Lefff (Lexique des formes fléchies du franc ¸ais -Lexicon of French inflected form) is a large-coverage syntactic lexicon for French [START_REF] Sagot | The Lefff , a freely available, accurate and large-coverage lexicon for French[END_REF]. 2 The current version of the Lefff (which is not the last one, as explained below) contains 10,214 entries for 7,813 distinct lemmas. Contrarily to the three other lexicons we have used, which were developped manually, the Lefff was developed in a semi-automatic way: automatic tools were used together with manual work [START_REF] Sagot | The Lefff 2 syntactic lexicon for French: architecture, acquisition, use[END_REF][START_REF] Sagot | The Lefff , a freely available, accurate and large-coverage lexicon for French[END_REF]. The Lefff relies on the Alexina framework for the acquisition and modeling of morphological and syntactic lexicons. To represent lexical information, an Alexina lexicon relies on a two-level architecture:

• the intensional lexicon associates (among others) an inflection table and a canonical sub-categorization frame with each entry and lists all possible redistributions from this frame;

• the compilation of the intensional lexicon into an extensional lexicon builds different entries for each inflected form of the lemma and every possible redistribution.

The version of the Lefff that was available before the experiments described below (version 3.0b) contains only one entry for the lemma vérifier. Here is a simplified version of this entry: It describes a transitive verb whose arguments have the syntactic functions Suj and Obj listed between angle brackets, and which allows for the functional redistributions past participle used as an adjective, active (the default distribution), impersonal middle-voice "se" construction, impersonal passive, and passive.

The different syntactic functions are defined in the Lefff by criteria close to that used in Dicovalence, i.e., they rely for a large part on cliticization and other pronominal features. The Lefff uses the following syntactic functions: Suj (subject), Obj (direct object), Objà (indirect object canonically introduced by preposition "à"), Objde (indirect object canonically introduced by preposition "de"), Loc (locative), Dloc (delocative), Att (attribute), Obl or Obl2 (other oblique arguments).

Each syntactic function can be realized by three types of realizations: clitic pronouns, direct phrases (nominal phrase (sn), adjectival phrase (sa), infinitive phrase (sinf), completive (scompl), indirect interrogative (qcompl)) and prepositional phrases (direct phrases preceded by a preposition, such as de-sn, à-sinf or pour-sa). Finally, a function whose realization is not optional has its realizations list between angle brackets. 3The way morphological and syntactic information is encoded it the Lefff is such that the Lefff be directly used in NLP tools. For example, we are aware of several parsers using the Lefff , and based on various formalisms: LTAG, including LTAGs generated from metagrammars developed in various meta-grammar formalisms (Thomasset and Éric de La Clergerie, 2005), LFG [START_REF] Boullier | Efficient and robust LFG parsing: SXLFG[END_REF], and less well-known formalisms such as Interaction Grammars or Pre-Group Grammars.

Lexicon-Grammar tables

In the Lexicon-Grammar [START_REF] Gross | Méthodes en syntaxe : Régimes des constructions complétives[END_REF]Boons et al., 1976b;Boons et al., 1976a;[START_REF] Guillet | La structure des phrases simples en franc ¸ais : Les constructions transitives locatives[END_REF], the 14,000 entries for verb lexical entries are structured in the form of 61 classes, each class being described in a different table . 4 Each class (table) is defined by a defining property, which is valid for all lexical entries belonging to the class (i.e., the defining property described a sub-categorization that is valid for all entries in the class, although other sub-categorizations might be also valid for a given entry).

Lexicon-Grammar tables include two entries for the lemma vérifier, which both belong to class 6. Let us illustrate the notion of defining property and the content of the corresponding Lexicon-Grammar table using these entries. The defining property for class 6 is N 0 V QuP , which means that all entries in this class are transitive and may have a finite or infinitive clause as the realization of the second argument in addition to the default noun phrase realization. Note that the notion of syntactic function is absent from the Lexicon-Grammar model. In table 6, 40 additional properties are "coded", i.e., each entry specifies whether it has each property or not, in the form of a matrix with one entry per row and one property per column. Among these 40 properties (the set of properties differs from one table to another), we can cite for example N 1 =: QuP ind (if the second argument is a finite clause, its verbal head is at the indicative mood) or N 1 =: N hum (its second argument can be human).

The two Lexicon-Grammar entries the lemma vérifier are associated (among others) with the properties shown below (including the defining property for class 6). However, the second entry is not yet coded: the only thing we know about this entry is that it satisfies the defining property. As for the first one, the properties we have indicated here show respectively that its periphrastic inflected froms are built using the auxiliary avoir, that the second argument (N 1 )

is not mandatory, that it can be realized (among others) as a finite clause whose verbal head is at the indicative mood, as a pre-verbal particle (a clitic pronoun) or as a non-human noun phrase, and finally that it can be passivized (the subject becoming a non-mandatory argument introduced by the preposition par). al., 1984). In order to identify the valency of a predicate (i.e., its dependants and their properties), the Pronominal Approach uses the relation that exists between so-called lexicalized dependants (realized as syntagms) and pronouns that "intentionally cover" these possible lexicalizations. Pronouns (and "paranouns", cf. below), contrarily to syntagms, syntactic functions or thematic roles, have two important advantages: (1) they are purely linguistic units, and do not have any of the properties (e.g., semantic properties) that make grammaticality judgements about sentences with lexicalized dependants difficult to motivate;

(2) there are only a limited amount of such units: their inventory is finite. Note that the pronouns used in Dicovalence are more numerous than what is usually called a pronoun. Indeed they also include what Dicovalence calls "paranouns", that differ from pronouns because they can be modified (as rien 'nothing' in rien d'intéressant 'nothing interesting') and because they can not be taken up by a syntagm (cf. *il ne trouve rien, les preuves 'He finds nothing, the evidences', vs. il les trouve, les preuves 'He finds them, the evidences'). In Dicovalence, pronouns are grouped in paradigms, which correspond only approximately to syntactic functions (e.g., P0 corresponds to the subject, P1 to the direct object, and so on). But Dicovalence contains more paradigms than the usual inventories contain syntactic functions. For example, it licenses a quantity paradigm (PQ), a manner paradigm (PM) and others. The version of Dicovalence used in the experiments described below6 consists in a list of 8,214 entries for 3,729 unique verbal lemmas. These lemmas and entries are explicitely chosen because they are reasonably frequent. lemma vérifier in Dicovalence. These two entries exactly correspond to the two entries found in the Lexicon-Grammar: The example in entry 85770 means 'I will check this piece of information before I publish it', and the example in entry 85780 'The experiment validated his hypothesis'.

The Lexique des Verbes Franc ¸ais

The LVF (Lexique des Verbes Franc ¸ais) is a dictionary of French verbs developed by Dubois and Dubois-Charlier [START_REF] Dubois | Les verbes franc ¸ais[END_REF]) that has the form of a thesaurus of syntactico-semantic classes, i.e., semantic classes defined using syntactic criteria. It is a very large coverage resource, that gathers syntactic and semantic information. The different classes, that contain 25,610 entries, are defined by both syntactic and semantic features and form a three-level hierarchy. At the lowest level of the hierarchy, sub-sub-classes are either homogeneous in terms of syntactic behaviour, or are divided once more in sets of entries with entries that all have the same syntactic behaviour. However, these syntactic behaviours are coded in a compact but abstruse way, that we shall illustrate on our running example.

In LVF, vérifier has three distinct entries. Two of them are in the sub-sub-class P3b of transitive verbs "of the type 'target one's thinking activity towards something'". It belongs to the sub-class P3 for verbs expressing the 'manifestation of a thinking activity towards somebody or something', which is a sub-class of the larger class P of psychologic verbs. The last entry belongs to class D of verbs like donner 'give', sub-class D3 containing verbs with a figurative meaning "giving something to somebody" or "obtaining something from somebody", sub-sub-class D3c of verbs meaning "granting validity to something or value to somebody". These entries contain, among other things, the following information:

vérifier LVF 1 P3b T1400 P3000 vérifier LVF 2 P3b T1300 P3000 vérifier LVF 3 D3c T3300
The third column contains the syntactic codes. For example, code T1400 indicates a transitive construction with a human subject and a non-human (nominal) or clausal direct object. Code P3000 a pronominal construction with a non-human subject. T3300 stands for a transitive construction with non-human nominal subject and object, whereas T3100 stands for a transitive construction with a non-human nominal subject and a human object. On the one hand, these examples show, although not very clearly, a general fact: syntactic descriptions in LVF are less fine-grained than those found in other resources, except for semantic properties of the arguments, in particular prepositional ones. On the other hand, one can see that the inventory of lexical entries is more fine-grained than in other resources: the first two entries introduce a distinction that is not present in Dicovalence or in Lexicon-Grammar tables, which puts them together in only one entry (vérifier DV 85770 and vérifier LG 6 504 ). The third entry directly matches entries vérifier DV 85780 and vérifier LG 6 505 ).

Merging algorithm

As sketched in the Introduction, our merging algorithm is a three-step process [START_REF] Sagot | Méthodologie lexicographique de constitution d'un lexique syntaxique de référence pour le franc ¸ais[END_REF]:

• converting all input resources into the common model, which, as explained above, is Alexina; all converted resources must use the same inventory of syntactic functions, realizations and redistributions -in our case, that of the Lefff ; the main challenge at this stage is to be able to extract as much information as possible from the input resources and encode them in the form of an Alexina lexicon, despite all discrepancies between resources, as seen in the previous section;

• creating clusters of entries from various resources such that the entries in each should be merged into one entry; this step is very challenging, as its aim is to address the discrepancies in the granularity of lexical entries from one lexicon to another; for example, it is reasonable to consider that entries vérifier DV 85770 , vérifier LG 6 504 and both vérifier LVF 1 and vérifier LVF 2 for a unique grouping • merging of these clusters into output lexical entries.

Converting input lexicon in the Lefff format

The way the lexical information is structured in the Lefff is not very different from what can be found in Dicovalence. This makes the conversion process for Dicovalence reasonably straightforward. It is based on the following principles, which are obviously approximations:

• each Dicovalence paradigm is mapped into a Lefff syntactic function; 7

• each pronoun (or paranoun) in the Dicovalence paradigm is mapped into a Lefff realization: for example, if the pronoun te belongs to paradigm P1, a realization cla (accusative clitic) is added to the syntactic function Obj (we lose here the fact that the direct object can be human);

• each Dicovalence reformulation is converted into a Lefff redistribution.

Converting Lexicon-Grammar tables into the Alexina format is much more complex a task. Although the extraction of an NLP-oriented lexicon from Lexicon-Grammar tables has raised interest for some time [START_REF] Hathout | Automatic construction and validation of French large lexical resources: Reuse of verb theoretical linguistic descriptions[END_REF][START_REF] Gardent | Maurice Gross' Grammar Lexicon and Natural Language Processing[END_REF], the only attempt that was successful in producing and using in a parser an NLP-lexicon from all lexicon-grammar tables is the work by Tolone and colleagues [START_REF] Tolone | Using Lexicon-Grammar tables for French verbs in a large-coverage parser[END_REF].

The final output of this conversion process is an Alexina lexicon that is consistent with the Lefff in terms of syntactic function, realization and redistribution inventories, and in terms of linguistic modeling. It is what we shall call the "full" Alexina version of Lexicon-Grammar tables. Because this conversion process is complex, and before its results were available, we have also directly extracted a "light" Alexina version of Lexicon-Grammar tables, in the context of the work about pronominal constructions described in Section 4.1. [START_REF] Sagot | Constructions pronominales dans dicovalence et le lexique-grammaire -intégration dans le lefff[END_REF]. For each entry we have retained for this work, we only extracted its functional sub-categorization frame, i.e., the list of syntactic functions without their possible realizations, as well as some redistributions (active, passive and se-middle).

Building an Alexina version of LVF was simply achieved by parsing valency data (codes such as T3100) and generating on-the-fly the corresponding Alexina entries.

The only pieces of information that required a few heuristics are syntactic functions, which are not all explicitely recoverable from LVF codes, but that can be inferred using LVF information of argument introducters (e.g., prepositions) and semantic types.

In the remained of this paper, E i n is the lexical entry n in the lexicon i after it is converted in the Alexina format. Thus, i can be Dicovalence, LVF or Lefff , as well as LG for the "full" Alexina variant of Lexicon-Grammar tables, and LGlight for the "light" version.

Grouping entries from various lexicons

For a given lemma, each resource might contain more than one entry. Therefore, it is necessary to determine the number of entries in the output merged lexicon, each of them being obtained by merging together one or several entries from each input lexicon, according to an algorithm detailed below. This means that the first step before merging is to build sets of entries for each lemma, each set corresponding to one output entry. We shall call such sets groupings. Building such groupings is a challenging task. Indeed, what we need here is to identify cross-resource correspondances between entries, that are not necessarily one-to-one. Moreover, we can only rely on the information that is available in the input resources, i.e., mainly syntactic information, whereas distinctions between entries are often, at least in part, semantic. On the other hand, we have seen, while describing our input resources, and in particular the example of the entries for vérifier, that these resources have various granularities. In the case of vérifier, we can see that LVF entries are more fine-grained than Lexicon-Grammar and Dicovalence entries, which in turn are more finegrained than the unique entry in the Lefff . It turns out that this ordering of the resources is the same for most verbal lemmas. Therefore, we have chosen to base our grouping algorithm on an inclusion relation, which formalizes this intuition. We first define this inclusion relation at the entry level as follows. For a given lemma, an entry E 1 is included in or more specific than an entry E 2 if and only if the set of clauses headed by an occurrence of entry E 1 is included in the set of clauses headed by E 2 . Such an inclusion is noted E 1 ⊂ E 2 . For example, vérifier DV 85770 ⊂ vérifier Lefff 1 . Then, we generalize this inclusion relation at the resource level. Contrarily to the inclusion relation at the entry level, which can be defined without any problem, assuming that we can define an inclusion relation at the resource level is obviously an approximation, but it is required for being able to create these mappings. A lexicon i is considered included in or more specific than another lexicon j if we make the assumption that entries from lexicon i are all more specific than entries from lexicon j, i.e., each entry E i n is more specific than an entry E j m , except if the lexicon j does not contain any entry corresponding to E i n (in that case, i's coverage is higher than j's); assuming that i is more specific than j, this means that building groupings can be achieved by computing inclusion relations of the form E i n ⊂ E j m . Generalizing the example given above, we can posit that DV ⊂ Lefff . We compute such inclusion relations using the following heuristics: starting from the resource-level hypothesis that i ⊂ j, an entry E i n is considered included in another entry E j m if it has exactly the same set of arguments with one of the base syntactic functions (subject, direct object, indirect object introduced by à, indirect object introduced by de) and if the set of syntactic functions of remaining arguments in E j m is included in that of E i n . One can see that we only rely on the inventories of syntactic functions. Moreover, we consider that only base synctactic functions can be used as safe clues, and that more oblique ones are likely to be found only in the most specific resource -but if one is found in the least specific resource, then it has to be in the specific one. In our case, this algorithm satisfyingly computes the relation vérifier DV 85770 ⊂ vérifier Lefff 1 , as they both entail, after conversion in the Alexina format, the syntactic functions Suj and Obj (the Alexina version of vérifier DV 85770 is shown below). The groupings are then build as follows: we start from each entry that includes no other entry (often, an entry from the most specific lexicon) and we follow all inclusion relations until we reach entries that are include in no other entries. The set of all entries that are reached constitute a grouping. Of course, a grouping might end up containing entries from one resource only, if it is not included in any entry from another lexicon. If this is because this entry corresponds to a meaning or a valency that is not covered by other resources, this is the expected result. However, it might be the result of mismatches resulting from the original resources, either because of errors in an input lexicon or because there are differences in the way a same construction is analyzed (e.g., a resource might consider as an indirect object in à what another resource analyzes as a locative argument). These problems, as well as the fact that entries might be incomplete (see the case of vérifier LG 6 505 ), might also provoke erroneous groupings. But this algorithm could still be improved. First, it can never put two distinct entries from the same lexicon in a same grouping. Going back to our running example, this algorithm would cluster all entries for vérifier in three groupings, each of them containing one of the three LVF entries, although we might wish only two. Second, restricting the information used for creating groupings to the inventory of syntactic functions is not always precise enough. In our running example, a correct mapping between LVF and Dicovalence entries for vérifier would require using the information about the human vs. nonhuman features applied to the subject. These improvements will be implemented in the future.

Merging entries

Once the groupings are built, we merge the entries in each grouping in a relatively straightforward way:

• the set of syntactic functions is built as the union of the set of syntactic functions in the input entries;

• for each syntactic function, the set of realizations is also obtained by union; for each realization we which sources include it (no indication is added if it is licensed by all entries in the grouping);

• a realization is considered mandatorily realized only if it is mandatory in all entries in the grouping,

• the set of possible redistributions is built as the union of all possible redistributions in all entries in the grouping.

Let us illustrate the output of the merging of a grouping containing the entries vérifier Dicovalence Note that the infinitive relalization of the direct object only comes from the Lefff , and is marked as such. This allows for a more efficient manual validation, if required, as a piece of information that is only licensed by one resource is more dubious than others. In this case, it is valid.

Merging experiments

4.1. Improving the coverage of the Lefff on pronominal entries with Dicovalence and Lexicon-Grammar tables In order to improve the coverage of the Lefff over pronominal entries and pronominal constructions (i.e., realizations using the reflexive or the reciprocal se), we have leveraged the syntactic information Dicovalence, and to a lesser extend, Lexicon-Grammar tables, under the "light" version described above [START_REF] Sagot | Constructions pronominales dans dicovalence et le lexique-grammaire -intégration dans le lefff[END_REF]. First, we have carefuly described such constructions, and explored the way they were encoded in Dicovalence and in Lexicon-Grammar tables, as well as the way they were to be formalized in the Lefff . Then, we have extracted from Dicovalence and converted in the Alexina formalism the 5,273 entries that are either pronominal or that include realizations in se. Moreover, we have extracted 550 such entries using the "light" conversion scheme. We have merged the Lefff as well as these two additional sets of entries, using the inclusion relations DV ⊂ Lefff ⊂ LG-light. The result of the merging, which has since then be included in the Lefff , consists in 5,464 lexical entries.

Merging the Lefff , Dicovalence and LVF entries

for denominal and deadjectival verbs in -iser and -ifier In French, verbs in -iser and -ifier are particularly interesting.

First, most of them are denominal or deadjectival verbs, which means they are relevant for studying the relation between morphological derivation and valency. Second, a large amount of verbal neologisms are built using one of these two morphological derivation mechanisms, and studying verbs in -iser and -ifier is an important step towards the development of tools for turning a syntactic lexicon into a dynamic lexicon that evolves in parallel with textual corpora. Our work [START_REF] Sagot | Description et analyse des verbes désadjectivaux et dénominaux en -ifier etiser[END_REF] was based on the Lefff , Dicovalence and on LVF, which has a very large coverage. We have restricted it to verbs in -iser or -ifier that are indeed denominal or deadjectival, by manually removing other verbs ending "accidentally" in -iser or -ifier, such as croiser 'cross'. We relied on the following inclusion relations: LVF ⊂ DV ⊂ Lefff. The merging process created 2,246 entries covering 1,701 distinct lemmas (1,862 entries for verbs in -iser covering 1,457 distinct lemmas, and 384 entries for verbs in -ifier covering 244 distinct lemmas. Note that this work was complemented with a corpusbased extraction step for finding missing denominal and deadjectival entries in -iser and -ifier.

Merging the Lefff and Dicovalence for increasing

the granularity and the accuracy of the Lefff In order, again, to increase the granularity and the accuracy of the Lefff , we have conducted a work aiming at merging the whole verbal lexicon of the Lefff and Dicovalence, and then validate or correct and/or merge manually the resulting entries. We have applied the methodology described in this paper using the inclusion relation DV ⊂ Lefff. The, we have validated the 100 most frequent lemmas as well as all dubious lemma, i.e., those lemma who got more entries in the merged lexicon than originally in both input lexicons. This validation step allowed us to remove erroneous realizations that were present in the Lefff , to indeed extend its coverage, accuracy, and fine-grainedness, but also to unvail errors in Dicovalence itself. This illustrates what we have explained above: not only merging syntactic lexicons lead to a improved output resource, but it also allows to improve the input resources themselves. This work extended the number of verbal entries in the Lefff from 10,214 to 12,610, whereas the number of distinct lemmas was extended from 7,813 to 7,990 lemmas. The new version of the Lefff resulting from this automatic merging and manual validation and correction step is already freely available in the last distribution of the Lefff , but is not yet considered validated enough to replace the previous verbal lexicon files, which are therefore still distributed as well. It corresponds to the NewLefff in the parsing evaluation work described in [START_REF] Tolone | Evaluating and improving syntactic lexica by plugging them within a parser[END_REF].

Conclusion and next steps

In this paper, we have shown how syntactic lexical resources can be merged, in order to take benefit from their respective strong points, and despite the differences in the way they represent syntactic lexical information. We have described four large-coverage syntactic (including valency) lexicons for French, among which the Lefff , and have shown how we have used our merging algorithm for extending and improving the Lefff . In two experiments, we have merged up to 3 resources but restricting ourselves to two classes of entries. In the last experiments, all entries of only two lexicons were merged. Moreover, we used one of our input lexicons, namely Lexicon-Grammar tables, only in a light way, as explained below. The next step of our work will be twofold. First, we will implement improvements that will address the limits of our grouping algorithm, as explained above. Second, we will finally merge our four lexical resources, including Lexicon-Grammar tables fully converted in the Alexina format (as opposed to the "light" version). This should give birth to a new version of the Lefff , which will then become the syntactic resource with the largest coverage, and hopefully a very high accuracy, concerning French verbs.

85770 and vérifier Lefff 1 .

 1 The Lefff entry has been shown above. The Dicovalence entry in its original format was also given. Once converted in the Alexina model, it has become: cla|qcompl|scompl|sinf Lefff |sn)>;%ppp employé comme adj,%actif, %passif impersonnel,%passif, %se moyen,%se moyen impersonnel

Table 1 :

 1 Table 1 shows both (simplified) entries given for the Entries for vérifier in Dicovalence.

	vérifier DV 85770

The Lefff is freely available under the LGPL-LR license. See http://gforge.inria.fr/projects/alexina/

Other information are encoded in the Lefff , such as control, mood for finite clause argument realizations, and others.

Lexicon-Grammar tables are freely available under the LGPL-LR license. See http://ladl.univ-mlv.fr/.

Dicovalence is freely available under the LGPL-LR license. See http://bach.arts.kuleuven.be/dicovalence/

It is the version labeled 061117, which is not the last version. Experiments about the last version of Dicovalence are planned.