Thanh-Trung Van
email: van@emse.fr

Michel Beigbeder

Hybrid Method for Personalized Search in Scientific Digital Libraries

Users of information retrieval systems usually have to repeat the tedious process of searching, browsing, and refining queries until they find relevant documents. This is because different users have different information needs, but user queries are often short and, hence, ambiguous. In this paper we study personalized search in digital libraries using user profile. The search results could be re-ranked by taking into account specific information needs of different people. We study many methods for this purpose: citation-based method, content-based method and hybrid method. We conducted experiments to compare performances of these methods. Experimental results show that our approaches are promising and applicable in digital libraries.

Introduction

Search in digital libraries is usually a boring task. Users have to repeat the tedious process of searching, browsing, and refining queries until they find relevant documents. This is because different users have different information needs, but user queries are often short and, hence, ambiguous. For example, the same query "java" could be issued by a person who is interested in geographical information about the Java island or by another person who is interested in the Java programming language. Even with a longer query like "java programming language", we still do not know which kind of document this user wants to find. If she/he is a programmer, perhaps she/he is interested in technical documents about the Java language; however, if she/he is a teacher, perhaps she/he wants to find tutorials about Java programming for her/his course.

From these examples, we can see that different users of an information retrieval system have different information needs. Furthermore, a person can have different interests at different times. A good information retrieval system have to take into account these differences to satisfy its users. This problem could be solved if the system can learn some information about the interests and the preferences of the users and use this information to improve its search results. This information is gathered in user profiles. Generally, a user profile is a set of information that represent interests and/or preferences of a user. This information could be collected by implicitly monitoring the user's activities [START_REF] Kelly | Implicit feedback for inferring user preference: a bibliography[END_REF][START_REF] Shen | Implicit user modeling for personalized search[END_REF] or by directly requesting the users [START_REF] Chen | Webmate: a personal agent for browsing and searching[END_REF]. User profiles could be used not only for personalized search [START_REF] Speretta | Personalizing search based on user search histories[END_REF], but also for different tasks like information filtering [START_REF] Seo | A reinforcement learning agent for personalized information filtering[END_REF] or personalized visualization of search results [START_REF] Singh | classification of web search results using personalized ontologies[END_REF]. In the frame of digital libraries, user profiles could be collected from the papers that the users read in this library, from users search histories, from users' browsing histories or be explicitly specified by user etc.

Our works focus on personalized search in digital libraries: the users' search results are re-ranked using similarities between documents in the search results and the user profile. Unlike most other personalized information retrieval systems that use only content-based methods to build users' profiles and to represent the documents in order to compute the similarities between them, we also use citation-based methods and hybrid method for this purpose.

The rest of this paper is organized as follow. In the next section, we present some related work. Then in the section 3 we present our approaches for personalized search in digital libraries. Experiments and results are presented in the section 4. Finally, conclusions and future work are presented in the section 5.

Related Work

The work of Amato et al. [START_REF] Amato | User profile modeling and applications to digital libraries[END_REF] presents a user profile model that can be applied to digital libraries. In this model, information about a user is classified in five data categories: i) the personal data category which contains the user's personal identification data ii) the gathering data category which collects preferences and restrictions about the documents the user is looking for iii) the delivering data category that are specifications about delivery modes of information iv) the actions data category which contains the recording of the user's interaction with the retrieval systems and navigation data v) the security data category which is a collection of user preferences establishing the conditions under which the data represented in the user profile may be accessed.

In [START_REF] Rohini | A collaborative filtering based re-ranking strategy for search in digital libraries[END_REF], the authors propose some approaches for re-ranking the search results in a Digital Library that contains digitized books. They consider two kinds of search: search for books by querying on the metadata of books (Metadata Search, MS) and search for informations in the pages of book by querying using keywords (Content Search, CS). They use two different profiles corresponding to these two kinds of search: MS-profile and CS-profile. A MS-profile is built from the ratings of the books that the user provides explicitly. A CS-profile is built from the content of the pages that have been judged as relevant by the user. Metadata search results and content search results are re-ranked using these profiles.

Torres et al. [START_REF] Torres | Enhancing digital libraries with techlens+[END_REF] present many algorithms for recommendation of research papers: collaborative methods, content-based methods and hybrid methods. The user profile represents short-term interests and consists of only one paper. The authors did many offline and online experiments to compare the performances of these methods and found many interesting results.

The CiteSeer digital library [START_REF] Bollacker | A system for automatic personalized tracking of scientific literature on the web[END_REF] that contains scientific papers uses a heterogenous profile to represent the user interests. If there is a new available paper, CiteSeer will try to decide if this paper would be interesting to the user using his/her user profile. If so, then the user can be alerted about this paper. CiteSeer uses two methods for determining paper relevance: i) constraint matching and ii) feature relatedness. The former method allows a user to describe what is an interesting paper by specifying contraints (e.g. keyword). In the latter method, the user specifies a set of papers that are interesting and CiteSeer tries to find papers that are related to this set using content-based method and citation-based method.

Our Approaches for Personalized Search in Digital Libraries

Our work focus on personalized search in digital libraries of scientific papers. Like in the CiteSeer system [START_REF] Bollacker | A system for automatic personalized tracking of scientific literature on the web[END_REF], the user profile is represented by a set of paper that are interesting to the user. Each time the user issues a query, the first n documents1 will be re-ranked using the original score computed by the search engine and the similarity between the document and the user profile. The document-profile similarity is computed using two methods: a content-based method and a citationbased method. The personalized search process is illustrated in Figure 1.

Results

Citation databases

Fig. 1. Re-ranking of search results using user profile

Computing Similarity Document-Profile

The similarity between a document and a profile is the sum of the similarity between this document and each document in the user profile:

similarity(d, p)= d ′ ∈p similarity(d, d ′ )( 1 )
The document-profile similarity is computed using two methods: a contentbased method and a citation-based method. We use the zettair 2 search engine to compute the content-based similarity (under the vector-space model). To compute the content-based similarity between a document and other documents in the collection, we use the zettair search engine to index the collection and submit the document as the query to zettair and note the returned similarities. Content-based methods are widely used to compute document-profile similarity in personalized information retrieval systems. However, one of the main problems with this method is that it favors only papers that are similar in content with the papers in the user profile, and not papers that may be different in content (e.g. using different terms) but related with them.

An important characteristic of scientific papers in a digital library is that they have bibliographical relationships between them. We can use these relationships to find the similarity between scientific papers. Content-based methods and citation-based methods are complementary to find relatedness between scientific papers. The citation-based similarity that we use is based on the principle of the co-citation method [START_REF] Small | Co-citation in the scientific literature: A new measure of the relationship between two documents[END_REF]. In this method, the relatedness between two papers is based on their co-citation frequency. The co-citation frequency is the frequency that two papers are co-cited. Two papers are said to be co-cited if they appear together in the bibliography section of a third paper. However if we want to know this citation information, we have to extract the citation graph from the actual library or to get this information from a citation database3 . Both methods are usually limited; i.e. we can only know citing papers of a paper A if the citing papers exist within the same digital library or citation database with the paper A. Many works [START_REF] Huang | Tssp: A reinforcement algorithm to find related papers[END_REF][START_REF] Couto | A comparative study of citations and links in document classification[END_REF] showed that if the size of a digital library or a citation database is not big enough, then the performance of this method will be limited.

Recently, a new method for citation analysis called Web citation analysis begins attracting the scientometrics community. Web citation analysis finds citations to a scientific paper on the Web by sending the query containing the title of this paper (as phrase search using quotation marks) to a Web search engine and analyze returned pages [START_REF] Vaughan | Bibliographic and web citations: what is the difference?[END_REF]. Because a Web search engine can index many kinds of document in many different formats, the notion of "citation" used here is a "relaxation" in comparison with the traditional definition. Vaughan and Shaw [START_REF] Vaughan | Bibliographic and web citations: what is the difference?[END_REF] used this method with the Google search engine and compared the method with the traditional bibliographic method using ISI database. Given an article, they classified Web documents that cite this article into 7 different categories: Journal (site of correspondence journal); Author (author, co-author, or one of their employers lists the articles in their pages); Service (a Web bibliographic service lists the article); Class (bibliography/reading list for a course); Paper (a scientific paper that is posted on the Web); Conference (conference announcement, report or summary/description); O t h e r( c i t e di na n o t h e rw a y ) .K o u s h a and Thelwall [START_REF] Kousha1 | Motivations for url citations to open access library and information science articles[END_REF] used a similar strategy called URL citations to find citations to articles of open access journals. However, in their work the URL citation of a Web page is the mentions of its URL in the text of other Web page (and not its title).

In our work, we propose to use the Web as a citation database to find the similarity between scientific papers. Our method is called Web co-citation method.

In the Web co-citation method, we compute the co-citation similarity of two scientific papers by the frequency that they are "co-cited" on the Web. The notion of "co-citation" used here is also a "relaxation" in comparison with the traditional definition. If the Web document that mentions two scientific papers is another scientific paper then these two papers are normally co-cited. However, if this is a table of content of a conference proceeding, we could also say that these two papers are co-cited and have a relation because a conference normally has a common general theme. If these two papers appear in the same conference, they may have the same general theme. Similarly, if two papers are in the reading list for a course, they may focus on the same topic of this course. In summary, if two papers appear in the same Web document, we can assume that they have a (strong or weak) relation. The search engine used in our experiment is the Google search engine. To find the number of time that a paper is "cited" by Google we send the title of this paper (as a phrase search using quotation marks) to Google and note the number of returned hits. Similarly, to find the number of times that two papers are "co-cited", we send the titles of these two papers (as a phrase search and in the same query) to Google and note the number of returned hits. This is valid because Google default is to use automatic "AND" queries. This idea is illustrated in Figure 2. In this example, we are looking for the co-citation frequency of two papers, the title of the first paper is "An adaptive Web page recommendation service" and the title of the second paper is "A hybrid user model for news story classification". Here the co-citation frequency is 11. In our experiments, we use a script to automatically query Google instead of manually using a Web browser. We use a variant of the formula presented in [START_REF] Prime-Claverie | Transposition of the cocitation method with a view to classifying web pages[END_REF] to compute the co-citation similarity between two papers:

cocitation similarity(d, d ′ )=ln cocitation(d , d ′ ) 2 citation(d)+citation(d ′ ) (2)
In Equation 2, cocitation(d, d ′ ) is the number of times that these two papers are co-cited 4 , citation(d)andcitation(d ′ ) are respectively the citation frequency that papers d and d ′ receive. Note that in the Web co-citation method, the document-profile similarity (cf. formula 1) has a negative value, we convert it into a positive value with same variation by this formula:

similarity ′ (d, p)= 1 |similarity(d, p)| (3)

Re-ranking Search Results

The final score that is used for re-ranking is a combination between the following scores: i) the original score computed by the search engine ii) the documentprofile similarity computed by the Web co-citation method iii) the documentprofile similarity computed by the content-based method. The combination formulas are the two following formulas:

-Linear formula:

final score = i α i × score i (4)
-Product formula:

final score = i score i (5)
In the formula 4, α i are positive coefficients that satisfy the condition i α i = 1. We tried many different combinations to find the best coefficients. The scores are normalized (divided by the correspondent maximal value) to have the values in the range from 0 to 1.

We conducted experiments to evaluate the performance of different combination methods. The experiments are presented in the following section.

Experiments and Results

The search engine that we use is the zettair search engine, the default model used in zettair is the Dirichlet-smoothing model [START_REF] Pehcevski | RMIT university at INEX 2005: Ad hoc track[END_REF]. The test collection that we use is the collection used in INEX 2005 5 . This collection has 17000 XML documents extracted from journals and transactions of IEEE Computer Society published between 1995 and 2004. Thus this collection could be used as a medium-size digital library of Computer Science. This collection includes not only scientific papers but also other elements like tables of content, editorial boards, etc. Because we are interested only in scientific papers, we have to remove these elements from the collection. After this process, the collection contains 14237 documents. Then we extract necessary information for our experiments from these documents. There are also many topics with relevance assessments distributed with the collection, each topic represents an information need. Two types of topics were used in INEX 2005 [START_REF] Malik | Overview of INEX 2005[END_REF]:

-CAS topics (Content And Structure) which allow users to use structure of documents in their queries. They contain explicitly references to the XML structure, and explicitly specify the contexts of the user's interests and/or the contexts of certain search concepts. -CO+S topics (Content Only + Structure) which do not contain structure of documents (however, it contains also an optional CAS title field which represents the same information need but including additional knowledge in the form of structural hints).

In our experiments we only use the CO+S topics to build the queries. There are 29 assessed CO+S topics but only 26 topics are used. The ignored topics are those that contain too few relevant document or are not typical queries in digital libraries (e.g. search for "call for paper"). Our work simulates personalized search using user profiles. We consider that each topic represents a different information need of one person. The user profile is built from the documents which are judged as relevant (participants in the TREC filtering task [START_REF] Hull | The trec-7 filtering track: description and analysis[END_REF] use similar strategies to build user profiles). We use a k-fold cross-validation approach [START_REF] Kohavi | A study of cross-validation and bootstrap for accuracy estimation and model selection[END_REF] for the evaluation. In this approach, the relevant documents of each topic are partitioned into k subsets (in our experiments, k = 5). The documents in a subset are used as test documents and the documents in the other k -1 subsets are used as the user profile. The experiment is repeated k times, each time a different subset is used as test subset. The evaluation metric is precision at n (with n = 5 10 15 20 30) and mean average precision (MAP). The precision at n is the percentage of retrieved docs that are relevant after n documents (whether relevant or nonrelevant) have been retrieved. The mean average precision is the mean of the average precision values of the set of queries. We use the trec6 program to compute these precision values.

Because there are k different experiments, hence there are k different MAP values and with each value of n there are k different precisions. Therefore, we have to compute the average values as follows:

Average of precisions at n = k i=1 precision at n i k (6) Average of MAPs = k i=1 MAP i k (7)
Results are presented in Table 1 and Table 2. With each table, the second column is the original results of the zettair search engine. The third column is the results of the re-ranking method using two scores: the original score of zettair and the citation-based document-profile similarity (computed by the Web co-citation method). The fourth column corresponds to the re-ranking method using the original score of zettair and the content-based documentprofile similarity (computed by the vector-space model using zettair). The fifth column corresponds to the hybrid re-ranking method using three scores: the original score of zettair, the citation-based document-profile similarity, and the content-based document-profile similarity. With each method, p means product combination (cf. formula 5) and l means linear combination (cf. formula 4). In the first method, the coefficients (used in linear combination) for the original score of zettair and citation-based document-profile similarity are respectively 0.5 and 0.5; in the second method, the coefficients for the original score of zettair and content-based document-profile similarity are respectively 0.25 and 0.75; in the hybrid method, the coefficients for the original score of zettair, the citationbased document-profile similarity and the content-based document-profile similarity are respectively 0.25, 0.20 and 0.55. From these results, we can see that all three methods can bring good amelioration. The content-based method is better than citation-based method. However, the hybrid approach is the best among the three methods, it brings +35.1% improvement with precision at 10 documents and 29.9% improvement with the mean average precision measure. In these experiments, the linear combination is better than the product combination. Furthermore, the amelioration seems to be more clear with precisions at 5, 10 and 15 documents.

Conclusions and Future Work

In this paper, we presented some methods for personalized search in digital libraries. In our approaches, the user profile which represent the user's interests is a set of papers. The user's search results are re-ranked using similarity between them and the user profile. We did experiments on a collection of IEEE papers used in the INEX 20005 campaign to compare the performances of the citationbased method, the content-based method and the hybrid method. Experimental results showed that these methods are efficient and the hybrid method is the best method. Our work is close to the work of Bollacker et al with the CiteSeer system [START_REF] Bollacker | A system for automatic personalized tracking of scientific literature on the web[END_REF]; however we focus on information retrieval while they focus on information filtering.

One of the future directions is to combine the bibliographic coupling method [START_REF] Kessler | Bibliographic coupling between scientific papers[END_REF] (another citation-based method) with these methods, which could lead to better performance. In the future, knowing that there are similar points between citations and hyperlinks, we intend to do similar experiments on a collection of Web pages to compare the performance of these methods in the hyperlinked environment.

Fig. 2 .

 2 Fig. 2. Illustration of the Web co-citation method

  The following topics are used in our experiments: 202 203 205 206 207 208 209 210 212 213 216 217 218 221 222 223 227 228 229 230 232 235 236 237 239 241.

Table 1 .

 1 Average of precisions at5, 10, 15, 20, 30 documents

	Result of Web Co-citation	Content-Based	Hybrid Approach
	zettair
	5d o c s 0.2892 0.3108 (p) (+7.5%) 0.3185 (p) (+10.1%) 0.3369 (p) (+16.5%)
	0.3185 (l) (+10.1%) 0.3462 (l) (+19.7%) 0.3631 (l) (+25.6%)
	10 docs 0.2123 0.2446 (p) (+15.2%) 0.2362 (p) (+11.3%) 0.2661 (p) (+25.3%)
	0.2477 (l) (+16.7%) 0.2715 (l) (+27.9%) 0.2869 (l) (+35.1%)
	15 docs 0.1672 0.1944 (p) (+16.3%) 0.1959 (p) (+17.2%) 0.2159 (p) (+29.1%)
	0.1974 (l) (+18.1%) 0.2174 (l) (+30.0%) 0.2221 (l) (+32.8%)
	20 docs 0.1473 0.1600 (p) (+8.6%) 0.1677 (p) (+13.8%) 0.1758 (p) (+19.3%)
	0.1639 (l) (+11.3%) 0.1815 (l) (+23.2%) 0.1781 (l) (+20.9%)
	30 docs 0.1154 0.1200 (p) (+4.0%) 0.1274 (p) (+10.4%) 0.1297 (p) (+12.4%)
	0.1215 (l) (+5.3%) 0.1374 (l) (+19.1%) 0.1408 (l) (+22.0%)

Table 2 .

 2 Average of MAPs

	Result of Web Co-citation	Content-Based	Hybrid Approach
	zettair
	Average 0.2631 0.2966 (p) (+12.7%) 0.2939 (p) (+11.7%) 0.3190 (p) (+21.2%)
	of MAPs	0.3017 (l) (+14.7%) 0.3207 (l) (+21.9%) 0.3391 (l) (+29.9%)

In our experiments n = 300.

http://seg.rmit.edu.au/zettair/

A citation database is a system that can provide bibliographic information about papers.

If two papers are not co-cited, we assign a small constant to avoid the zero value.

http://inex.is.informatik.uni-duisburg.de/2005/

http://trec.nist.gov/trec eval/

Acknowledgement

This work is done in the context of the European Project CODESNET and supported by the Web Intelligence Project of the "Informatique, Signal, Logiciel Embarqué" cluster of the Rhône-Alpes region.


Michel Beigbeder

Structured Content-Only Information Retrieval Using Term Proximity and Propagation of Title Terms

Structured Content-Only Information Retrieval Using Term Proximity and Propagation of Title Terms

Michel Beigbeder

École Nationale Supérieure des Mines de Saint-Étienne michel.beigbeder@emse.fr Abstract. Our experiments in the 2006 INEX ad'hoc track were based on the use of the proximity of the query terms in the documents to rank them. More precisely we define around each occurence of a query term an influence function. For an occurence appearing in the text itself, this influence function is linearly decreasing from 1 to 0 depending on the distance to the occurence. When a query term happens to appear in a title of a structured document its influence is uniformly 1 from the beginning to the end of the (sub-)section. We use boolean queries and these influence functions are combined according to the tree of a query using fuzzy logic. The score of any part of a document is the summation of the resulting influence function at the root of the query tree on the range of this part. We present and comment the results.

Introduction

The needs for information retrieval are now quite well established and the tools have a large acceptance from the users. Though quite every documents are created with some structure in mind, the methods and tools are mainly dedicated to flat documents as opposed to structured documents. Moreover most of the methods used for information retrieval on flat texts don't even take into account the basic structure of text: its linearity. In fact they are based on frequencies of terms (both in the documents and in the collection) and on the document lengths. Though there were some attempts to use the position of word occurences in the text with either explicit proximity operators in the query language or ranking based on proximity of the query terms. These attempts are reviewed in section 2.

Concerning the logical structure which is the structure commonly refered to when speaking about structured documents, it is only quite recently that a sufficiently widespread representation for it is available so that large corpora of structured documents are available. So it is now possible to experiment in the large some of the ideas developped for structured information retrieval in the past and to design new methods.

We present in this paper an extension to structured documents retrieval of a proximity based method originally dedicated to flat texts. Our model can easily compute a score for any segment of text, in particular for any section or the whole document. First in section 3, we present the document model this method deals with, and in section 4 the method itself. In section 5 we present the experiments made within the INEX 2006 campaign.

Proximity Use in Flat Document Retrieval

The idea of using the proximity of the query keywords for retrieving flat documents was first implemented in boolean systems with a NEAR operator. This operator itself was an extension of the ADJ operator. These two operators can be used between keywords in a boolean query and its truth value is related to the positions of the two connected keywords. The NEAR operator evaluates to true if the two terms appear within k words of each other (k is one for the ADJ operator).

The motivation for the ADJ (resp. NEAR) operator is to be able to describe in the query the needs for phrases (resp. loose phrases). These operators still are in use in tools used for searching in library catalogs. Though, from a technical point of view, they suffer from two handicaps that slowed down their use in plain text search engines. The first one is that they are closely linked to the boolean retrieval model which does not allow to rank the retrieved documents. The second one is that they do not fit well in the boolean query language model itself because they can only connect keywords and cannot be consistently extended to connect boolean sub-expressions.

More recent ideas for using keyword proximity were developped and they don't have these two limitations. Concerning the second one, the queries accepted by the query language model are either bags of terms or classic boolean expressions (only AND and OR operators). About the first one all the methods score the documents with respect to the positions of the keywords occurences, taking into account their proximity. We will now describe the basis of some of these methods

Interval Based Methods

For their participation to the TREC-4 campaign, both Clarke and al.

[1] and Hawking and al. [START_REF] Hawking | Proximity operators -so near and yet so far[END_REF] developped similar methods to rank text documents according to the proximity of the query keywords. The ideas are to select some intervals of text that contain all the keywords; to attribute a score to these intervals (the shorter the interval, the greater the score) and to sum up all these scores to score the document.

The two methods differ in the selected intervals: for Clarke and al. intervals cannot be nested because only the shortest ones are selected. For Hawking and al., for each occurence of any of the keywords, the shortest interval that contains all the keywords is selected. So if there are two successive occurences of the same keyword without any occurences of any other keyword in between, two nested intervals are selected.

The two methods also differ in the interval scoring, Clarke and al. chose a score that is roughly inversely proportional to the interval length and Hawking and al. chose a score roughly inversely proportional to the square root of the interval length.

The idea of using intervals was then revisited by Rasolofo and al. [START_REF] Rasolofo | Term proximity scoring for keyword-based retrieval systems[END_REF]. They chose to base their method on Okapi and they add an additional score to the Okapi probability. This additional score is based on the intervals containing any query terms pair: Each of the intervals shorter than a specified constant (6 in their experiments) that contains occurences of two query terms contribute to this additional score.

Fuzzy Influence Function Model

Beigbeder et al. [START_REF] Beigbeder | An information retrieval model using the fuzzy proximity degree of term occurences[END_REF] developped a retrieval model based on the fuzzy proximity of the keywords. More precisely each occurence of a keyword has a fuzzy influence on its neighbouring. This influence reaches its maximum value one at the keyword occurence position and decreases with the distance to this position. The most simple function that have this behaviour is a triangle function. Moreover there is an easy way to define a control parameter in such a function: its width, the length of the triangle basis. We will call k half of this length, it controls the range of the influence of an occurence.

Given a term the influences of its occurences are combined with a maximum operator. If the influence function is symmetrical, it consists in considering that at a given position the influence is determined by the nearest occurence of the term.

Their query language model is that of the classical boolean model with AND, OR and NOT operators (neither NEAR nor ADJ). The influences of the query terms are combined in the query tree according to the fuzzy logic interpretation of the union and intersection operators Let us consider an example with the document XXXXAXXXBXXX XXwhere there is an occurence of the term A (resp. B) at position 5 (resp. 9) and where X denotes any term different from the terms A and B. Figure 1 shows the proximities to the terms A and B in this sample document (with k =5)and their combination with a minimum corresponding to the AND operator.

Finally the score of a document is the summation of the influence function over all the positions in the text. It consists in evaluating the area under the curve associated to the root of the query tree. With our example, this is the area under the triangle of the curve A AND B. This is this model that we extended to some kind of structured documents.

Our Model of Structured Documents

Our work is pragmatic with respect to the structure of documents. We want to take into account the basic structure of many kinds of document models: nested sectionning and titles. This is the basis for scientific articles and technical documents but also for many more informal documents. We ignore any other structure, such as lists and emphasis for instance. As a particular case, we consider that a document is the highest level in the sectionning hierarchy. Another point is that sectionning and titles are tightly related so that in the L A T E X styles, only sectionning commands (\section, \subsection,...)are available and the titles are given as parameters to these commands.

So the basis for our document model is the family of document which could b ec o d e di nt h eL A T E X styles with the sectionning commands only. Here Formally the grammar of our document model is:

document = section section = '
''<title>' title text '</title>' section content '
' section content =( section text | section )*

Influence of Keywords Occurences

In the model presented in section 2.2, the influence of a term was only modelized for linear text. With our model of structured document, we have to modelize the influence of an occurence of a query term depending on the structural part in which this occurence does appear. As our document model is very simple there are only two cases: Occurences can appear in section text parts or title text parts.

For a term occurence which appears in the section text parts, the basis is the same as in linear text: A decreasing value of the distance to the occurence. But we add another constraint, the influence is limited to the section text part in which the occurence does appear.

Let us consider a document with the same text than the sample document of section 2.2 but with some structural tags:
 <title> X XXXA </title> X X X BXXXXX
. The occurence of the term B is in the section text part of the section. Figure 2 shows the limitation of the triangle proximity to the term B in the document to the surrounding section.

For term occurences which appear in the title text parts their influence is extended to the full content of the section and recursively the subsections contained in the corresponding section content part.

Considering our sample structured document the occurence of the term A is in the title text part of the section. Figure 2 shows the propagation of the influence of the occurence of the term A that appears in the title to the whole section.

Otherwise, like in the model presented in section 2.2, we use a boolean language query model and we combine the influence functions with min and max operators on the internal nodes of the boolean query tree. The basic score of a section is the summation of the influence function at the root of the query tree. We normalize this score by the maximum score reachable for this section. As the maximum value of the influence function is one. The maximum score simply is its length. Note that this maximum can actually be reached, for instance if all the query terms appear in the title of a section. The main part of the conversion consists in keeping the text and the section and title tags with their corresponding closing tags. This can easily be done with an xslt processor, but some non obvious choices have to be made about the textual content, particularly concerning the spaces. Unfortunately, at the syntactic level no right choice can be made because of an inconsistent use of some tags. For instance, the document number 1341796 contains the following highligth (the attributes of the collectionlink tags are removed):

This is a <emph3>List of <collectionlink ...>poison</collectionlink>ings</emph3>in alphabetical order of victim. It also includes confirmed attempted and fictional poisonings. Many of the people listed here committed or attempted to commit <collectionlink ...>suicide</collectionlink>by poison; others were poisoned by others.

The question is to insert or not a space after the closing of the collectionlink tag. If a space is inserted, the following text is generated (mistake is emphasized):

This is a List of poison ings in alphabetical order of victim. It also includes confirmed attempted and fictional poisonings. Many of the people listed here committed or attempted to commit suicide by poison; others were poisoned by others.

which is correct for the second instance of the tag, but not for the first one. If no space are inserted, the following text is generated:

This is a List of poisonings in alphabetical order of victim. It also includes confirmed attempted and fictional poisonings. Many of the people listed here committed or attempted to commit suicideby poison; others were poisoned by others.

with the reverse correctness. Notice that a choice about spaces are to be made for each tag, and in the above examples, the emph3 tags were replaced by spaces.

As no consistant choice could be made, we chose to insert space for each tag.

Indexation Tool and Index Structure

We used as a basis for the indexation the tool Lucy1 in version 0.5.4. Though it is an outdated version that is now replaced by different versions of zettair2 , we had some experience with it as we extended it with an implementation of the model presented in section 2.2. It is a good basis because it keeps within its index the position of every occurence of every term, and its lexical analyzer can recognize the syntax for any XML tags. At the indexation phase, we added the code necessary to keep track of the position and the nesting of the section and title tags. Remember that all other tags were removed in the previous step when the documents were converted to our model.

Building the Queries

Queries could be automatically built with the conjunction of the terms that appear in the title field of the topics. As our method is highly selective, there would be very few results if any in the retrieved list of documents with such queries. So either the basic conjunctive queries or the retrieval procedure have to be relaxed in some way. Keeping these automatic conjunctive queries it is possible to enlarge the result set by using a lemmatization both in the indexation phase and in the query analysis. We didn't try this solution but we chose to build the queries manually. With a basis of the conjunction of the terms found in the title field, sometimes, some terms were removed, but more often, these terms were expanded with disjunction of variations of the terms. These variations could simply be flexionnal ones (plural vs. singular) or derivationnal ones (verb, noun, adjective) or even semantic ones (synonyms or related concepts).

For instance, the title field of the topic number 289 is emperor "Napoleon I" Polish With a simple conjonction, the query could be (the '&' symbol is used for the boolean AND operator):

emperor & Napoleon & I & Polish
But some relaxation of it can be derived, for instance:

emperor & Napoleon & Polish Napoleon & Polish Napoleon & (Polish | Poland)
By using the description, narrative and ontopic keywords fields, other queries can be formulated, for instance:

Napoleon & (Polish | Poland | Laczynska | Malewski | Poniatowski)
We built two sets of queries, the short ones where we used only the title keywords with very minor expansion, and the enhanced queries for which we used terms from any field with expansions.

Runs

Given the queries and the value of the parameter k, our method is able to compute the fuzzy proximities to the query terms for each leaf of the query tree and to combine these influences up to the root of the tree. With our two query sets we used two values of k, 50 and 200. As only three runs could be submitted, we sent as official runs these combinations except the one with enhanced queries and k = 50.

Then a score can be computed for any span in the document. We computed scores for the whole documents and all its sections and subsections recursively. This was our participation to the Thorough task.

For the Best In Context task we searched for the maximum of the influence function at the root of the query tree and returned the section of highest level which contained the position at which this maximum was reached.

Finaly for the Focused task, the different parts of the documents were sorted with two keys: first, the score of the document to which it belongs; and then its own score.

6R e s u l t s

Whatever the task, our best results were obtained with our set of enhanced queries. With the set of short queries, our two sets of results with k =5 0a n d k = 200 are very close one to each other and quite worse than with enhanced queries. This shows that the expansion mechanism is mandatory. More experiments should be done on the value of k to draw some conclusion. In the following all the results are related to the set of enhanced queries. Figure 3 shows our results summarized with the ep-gr metric for the set of enhanced queries and k = 200. At the first level, the results are quite good but there is a rapid decrease in quality and it is very near from zero after 0.1. The same behaviour is seen in the results for the focused task either with overlap On (Fig. 4) or Off (Fig. 5). Moreover these figures display the result measure at 5 documents. When looking further in the list of results, our method compare less and less favourably to other methods. We develop some explanations about this remark in the following.

Our queries are conjunctions of the terms that a (part of a) document has to contain to have a non zero score. Moreover the occurences of the different terms must be close one to each other. As a result this method is highly selective. Table 1 displays the distribution of queries in regards to the length of the result list built by our method. This highlights the fact that our list of results are quite short. The length of the lists are longer as k increases -which is trivial -and longer also with the enhanced queries as most of them relax the constraints on the retrieved documents. Also it can be seen that the very large majority of our result lists are much shorter than the limit of 1500 imposed by INEX.

It seems though that we obtain a quite good precision. Our summarized results were also disavantaged because of our simplification of the structure. In the thorough task we only returned elements tagged as sections and did not add higher level elements when one section was retrieved.

Finally our propagation mechanism was disturbed by strange documents. For instance document number 192509 contains many text in lists between the <title> and </title> tags. This results with our structure simplification with a very long title whose terms are propagated to the whole title and its associated section. If it happens that all the query terms appear in this erroneous title the section reach a maximal score of 1. Besides this "document bug", it highlights ad r a w b a c ko fo u rm e t h o d .

7C o n c l u s i o n

We presented in this paper the ideas used for our participation to the INEX 2006 campaign in the ad'hoc retrieval task. Our method is based on the proximity of the keywords in the document and the propagation of the title words to the whole associated section. We obtained quite good precision results. The summarization done by the official measure was at our disadvantage because our result lists were very short and because we simplified the structure so we did not return every kind of elements. We also found that this method needs query expansion mechanism. Further works are to be done to study the best setting for the parameter k which controls the range of influence of a term in the text. Also some work has to be done to improve the propagation mechanism of title words which is very crude at the time.

Fig. 1 .

 1 Fig. 1. Proximities to the terms A and B and their combination for the query A AND B: x-axis: position of words in the text, y-axis: fuzzy proximities

Fig. 2 .

 2 Fig.2. Limitation of the influence of an occurence to the section text part in which it appears (proximity to the term B); propagation of a title term (proximity to the term A): x-axis: position in the text, y-axis: fuzzy proximities

Fig. 3 .

 3 Fig. 3. INEX 2006: Results' Summary, Metric: ep-gr, Quantization: gen, Task: thorough, Run: title Q Prox200NT02

Fig. 4 .

 4 Fig. 4. INEX 2006: Results' Summary, Metric: nxCG[5], Quantization: gen, Task: focused, Overlap=On, Run: title Q Prox200NF02

Fig. 5 .

 5 Fig. 5. INEX 2006: Results' Summary, Metric: nxCG[5], Quantization: gen, Task: focused, Overlap=Off, Run: title Q Prox200NF02

Table 1 .

 1 Distribution of queries as a function of their result list length

		No result 1t o1 010 to 100 100 to 1000 more than 1000
	Thorough k=50 short	13	21	50	37	4
	Thorough k=200 short	7	20	46	44	8
	Thorough k=200 enhanced		7	53	55	10
	Fo c./Best k=50 short	13	40	48	22	2
	Foc./Best k=200 short	7	30	60	26	2
	Foc./Best k=200 enhanced		15	75	32	3

http://www.seg.rmit.edu.au/lucy/

http://www.seg.rmit.edu.au/zettair/