International audienceThe Aquarelle1 architecture is designed and built in a user oriented perspective. Folder archiving, indexing and retrieval must thus be designed for data access through «user friendly», yet expeditious, methods. This is a major challenge to the Information Systems in the Aquarelle organisation. This document addresses the role of advanced full text indexing in delivering data access and archiving applications. Our aim is to investigate the effective use of structured folders using automatic or assisted indexing in a multimedia and multilingual environment

Bonhomme, Patrice

Romary, Laurent

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A Contrastive Indexing Method and its Integration in Aquarelle Folders

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HAL Id: inria-00525130https://hal.inria.fr/inria-00525130Submitted on 11 Oct 2010HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.A Contrastive Indexing Method and its Integration inAquarelle FoldersPatrice Bonhomme, Laurent RomaryTo cite this version:Patrice Bonhomme, Laurent Romary. A Contrastive Indexing Method and its Integration in AquarelleFolders. Cross-Language Information Retrieval - third Delos workshop, Mar 1997, Zurich, Switzerland.￿inria-00525130￿A Contrastive Indexing Method and its Integration in Aquarelle Folders28 March 1997 1A Contrastive Indexing Method and its Integration in Aquarelle FoldersP. Bonhomme & L. RomaryCRIN/CNRS & INRIA Lorraine - FRANCE.The Aquarelle1 architecture is designed and built in a user oriented perspective. Folderarchiving, indexing and retrieval must thus be designed for data access through «userfriendly», yet expeditious, methods. This is a major challenge to the Information Sys-tems in the Aquarelle organisation. This document addresses the role of advanced fulltext indexing in delivering data access and archiving applications. Our aim is to inves-tigate the effective use of structured folders using automatic or assisted indexing in amultimedia and multilingual environment.1.0  General PresentationOne of the main objectives of the Aquarelle Project is to provide a user with the technical facilities supportinginformation retrieval by querying. And our specific task at the CRIN-CNRS & INRIA Lorraine (within WorkPackage 6 [6]) is to design a Full Text Retrieval System, including mechanisms for automatic indexing of docu-ments using the information available within texts dealing with these documents. The prototype we are develop-ing, based upon a new method we called Contrastive Full Text Indexing, has been integrated within the GrifSGML Editor and tested on two different protocols:• Blind (or free indexing), that is with no reference to a given thesaurus. This has the advantage of providingnon technical words which a user could employ within his request.• Oriented (or controlled) indexing, with an existing thesaurus containing words close to the artwork beingindexed. This collocation word method is a ˝contrastive˝ indexing method [2].This task is a collaboration between INRIA, GRIF and FORTH/ICSIn addition, since Aquarelle is to support Multilingual or Crosslingual Information Retrieval, it is important, andcrucial for perpetuating the Aquarelle Project, to supply the users with multilingual thesauri if they exist or to con-tribute to the translation of the thesaurus. We achieve a tool which automatically aligns a text and its translation[5, 1]. This tool will assist the translation of thesauri resources across language.This task is a collaboration between INRIA, FORTH and ILSP2.0  Full Text Indexing and Contrastive MethodOur aim is to facilitate the effective use of structured folders using automatic or assisted indexing of SGML docu-ment.2.1  General Method for an Indexing SystemWe want to provide a key word extraction to index the textual content of the different documents of a given collec-tion or the sub-documents (from an SGML point of view) of a single document. The following indexing method isbased on the number of occurrences of tokens in the documents of a collection:1. Use of the SGML structure for each document (or sub-document) to locate textual data. Where appropriatecontent identifiers (tokens/terms) might be found? - text, description,... - titles, abstracts,...1. The Aquarelle Project is managed by ERCIM, the European Research Consortium for Informatics and Mathe-matics. Telematics Application Programmme, Information Engineering Sector IE-2005. http://aqua.inria.frA Contrastive Indexing Method and its Integration in Aquarelle Folders28 March 1997 22. Extraction of content tokens with or without words stemming or lemmatization.3. Assignment of term weights depending on term importance using:• The Inverted Document Frequency (IDF) weights• The Reduced Deviation (RD) method to supply user with indications about words occurring more than themean around a given hub word-type, token or term. This probabilistic method is used in corpus linguistic stud-ies, collocation word extraction and more generally in semantics and lexicology.2.2  Contrastive Full Text Indexing Method (CFTI)2.2.1  General FrameworkThe Aquarelle users are supposed to be museum curators, urban planners, commercial publishers and researchers.All users should be able to collect the information relevant to their need or answering their curiosity, wherever theinformation components are located with the aim of organising exhibitions and producing such information prod-ucts as books or CD-ROM. The amount of information increase the need, for a professional user, in informationretrieval and extraction and in content analysis. It is necessary to get rid of the non relevant information by eitherfiltering with a thesaurus and some authority file supports or with a semantic access to the plain text.2.2.2  Specification for a Contrastive ApproachThe meaning of a word isdecidable with its linguistic and/or situational context. We use the test of statistical sig-nificance to filter the context and to reveal a set of words occurring around a specified word (a hub-word) within ashort span in a text (a window). Words co-occurring around a hub-word share some recurring semes organized instructures. We observed that the test of Reduced Deviation could assist the thematic retrieval within a collection ofdocuments or within parts of a document. The main principle of a contrastive approach is to compare (or to con-trast) a reference collection of texts with a working collection of textual data. The working collection extractedfrom the reference collection, is a subset of the whole reference collection. The extraction of the working collec-tion can result from:• A text windowing procedure,• A selection of some SGML elements (and their content).Reference Collection Working Collectiontext windows (+/- tokens)A Contrastive Indexing Method and its Integration in Aquarelle Folders28 March 1997 32.2.3  Theoretical PrincipleThe goal of this method is to supply user with indications about words occurring more than the mean around agiven hub-word or term. It is left to the user to decide which among those words are semantically linked with thegiven hub-word. In this case information uniformly distributed (empty words, more frequently words,...) will notappear in the result of a Reduced Deviation procedure. This kind of non information is of course non relevant forthe document and is also language independent. The remaining words will be more relevant compared with otherdocuments (or other parts in the same document).2.2.4  Probabilist PrincipleOur model is based on a probabilistic indexing method which is a statistical assumption about distribution ofwords in a document. Our hypothesis is that a hub-word (can be a term) has an influence on a given word oranother term (a target word). We would be able to determine if a given word (target) co-occur more than the meanaround the hub-word. To accept or to reject this hypothesis implies that we take a risk corresponding to the possi-bility that the target word is not linked to the central word and that its frequent occurrence around the central wordis not due to a coincidence. We calculate, to evaluate the risk, what we call: a reduced deviation. We assume,according to our hypothesis, that words occurring around the central word have the same distribution than thewhole text. We introduced a normal approximation of the distribution: N(0,1). We calculate the reduced deviationrankr of a wordm:(EQ 1)with: (EQ 2)The more important the value (absolute) of the reduced deviation is, the more important the likelihood of thegiven word will correspond to a semantic link with the hub-word.Given this, we can provide a contrastive indexing in comparison to other documents in the collection or to sub-document (part) in the document.2.3  Controlled Indexing2.3.1  Using a Thesaurus or Authority ListA hub word can result from an IDF processing or come from a relative thesaurus. Thesaurus can be used tobroaden the indexing term by replacing them with the corresponding thesaurus class identifiers or by adding tothem the thesaurus class identifiers:• choose broader, related or narrowed terms,• term associations, term classification,• facets or/and hierarchies.2.3.2  Expert-user refinement by handIt is important for the user, to have the possibilityto drive his own indexing procedure, by changing the parame-ters of the system:1. Statistic parameters (size of the windows, threshold, working and reference corpus,...)rFreqobserved Freqtheor–Freqtheor 1 pm–( )×----------------------------------------------------------------=pmfrequency of the word m in the reference collectionFreqtheor---------------------------------------------------------------------------------------------------------------------------=A Contrastive Indexing Method and its Integration in Aquarelle Folders28 March 1997 42. Linguistic and document parameters (lemmatization or not, stemming words, list of words, definable SGMLdata structure, working corpus/document, reference corpus/document,...).2.4  An experimentA series of experiments are conducted to study the design issues of the Contrastive Indexing system within theGrif SGML Editor. The objectives of this experiment were:• To implement two different and complementary indexing methods, a global method(IDF) and a local term method (RD);• To fit statistical thresholds and their influences on results indexing;• To find the optimum numbers of document and size of documents in number of characters, words, lines,...• To establish stop list to removefluff words or use IDF to remove high-frequencyterms non relevant for indexing.This experiment have been tested on:• A set of 30 folders1 in SGML format (description of the city of Cognac),• A set of documents extracted from theMérimée data base2 and converted from HTML into SGML/TEI.1. coming from theInventaire Général and encoding with the CI DTD provided byEuroclidTABLE 1. Example of token extraction in the Collection CognacCollection COGNAC - Target Word: cognac Collection COGNAC -  Element: <DESC>RD TOKEN Fq. WIN Fq. TXT RD TOKEN Fq. WIN Fq. TXT24.8104 institut 28 93 11.0399 maclou 20 8021.6968 cognaçais 30 86 10.4327 autel 22 8215.7159 archéo 36 73 9.96775 église 39 11815.6222 charentes 19 50 8.57429 paroissiale 21 7014.0965 municipal 15 40 8.43421 escalier 20 6711.7747 poitou 17 37 8.4182 saint 101 22011.3867 musée 17 36 7.8329 périphérie 20 6411.3519 saint 125 128 7.18366 support 22 659.9904 élévation 25 41 7.17207 hermanowicz 34 899.5808 périphérie 36 50 5.92809 lambert 27 689.37531 antérieure 12 25 5.83096 sud 30 739.34375 léger 22 36 5.5313 hôtel 25 628.77337 maître 14 26 4.96011 creuse 17 448.34589 histoire 14 25 4.79652 figure 19 477.91525 détail 19 29 4.76852 distillerie 18 457.11915 françois 50 51 4.67325 poitou 13 357.11911 élévations 17 25 4.65019 charente 80 1467.11704 paroissiale 35 40 4.49553 moellon 16 406.60507 civat 12 19 4.30795 calcaire 17 416.03496 hôtels 35 36 4.2064 paris 36 735.82131 église 59 51 4.20493 couvert 14 35A Contrastive Indexing Method and its Integration in Aquarelle Folders28 March 1997 53.0  Integration in Aquarelle FoldersThe access to the Aquarelle System is gained by means of a User Client. The Aquarelle User Client offers a docu-ment authoring environment for professional usage. It provides two kinds of connection to the Aquarelle System:1. A full access through the Aquarelle Access Server on which the user is registered;2. A more restricted access through a WWW client.In the User Client, a user can have access to details of the informations that is available (in his language or not),using query composition and submission facilities, SGML editing and obviously access to Archive and FolderServers. It’s important for a user to have an interface with the Access Server to retrieve what resources are availa-ble and what resources are relevant to work on them. In that aim, the User Client provides query and retrieval util-ities to specify queries that identify such relevant documents.Within Aquarelle, a folder is considered as an SGML document. In his User Client environment, a user has anAuthority Editor System for creating, editing and modifying their structured documents or folders. The GrifSGML Editor is used for folder editing using some specific DTDs and the material for composing a folder beingedited is stored into the Client workspace. At the publishing time, to made available the folder in the Folder Serverby the Access Server, workspace folder, sub-folders and links are turned into Aquarelle folders and links. At thattime, user has the possibility directly from the SGML Editor to launch a keyword extraction procedure on hisfolder. The result will be stored in the folder profile itself.We used the Grif Application Toolkit Environment (GATE) to develop the CFTI system. The GATE API enables:• To create of integrated SGML authoring environments,• To customize Grif SGML Editor with new menus and functions,• To develop Document Oriented Interfaces.4.0  Multilingual Resources Features and LimitationsUsing and producing language resources to support querying and retrieval is a crucial point in the Aquarelle mul-tilingual environments. It includes the use of both trends in language engineering:2. http://www.culture.fr/cgi-bin/mistral/merimee5.15833 prieuré 11 15 4.20493 commerce 14 354.92605 commerce 14 17 4.02458 toit 20 454.82293 ville 69 52 3.9654 ensemble 14 344.39818 portail 13 15 3.92757 retable 13 324.20409 maulny 33 28 3.89277 sculpture 12 304.00413 léger 23 21 3.82414 martell 20 443.7119 hôtel 26 22 3.7681 françois 50 923.64024 ensemble 20 18 3.37964 prieure 15 353.63245 autel 40 30 3.24144 oeuvre 23 463.4955 chapelle 16 15 3.05144 vierge 11 253.48562 place 34 26 11.0399 maclou 20 80TABLE 1. Example of token extraction in the Collection CognacCollection COGNAC - Target Word: cognac Collection COGNAC -  Element: <DESC>RD TOKEN Fq. WIN Fq. TXT RD TOKEN Fq. WIN Fq. TXTA Contrastive Indexing Method and its Integration in Aquarelle Folders28 March 1997 61. the statistical trend: allowing for higher precision, it can be used as an automatic start-up in language process-ing and can be improve by an expert-user refinement.2. the semantic trend: allowing for a broader coverage using knowledge bases [4], thesaurus and overriding theinitial automatic results when needed.To implement multilingual querying using one of this approach (or both), it is necessary to give the correspondingtranslation of each thesaural term for each new language recognized. But two main problems remain since:• concepts expressed by one single term in one language sometime are expressed by distinct terms in another,• concepts (or specific terms) expressed in one language sometime are not expressed in another.The difficulty of the multilingual thesaurus construction resides in inter-cultural differences of concepts reflectedin the thesaurus structure. Terms at the leaf-level are very difficult to translate. In that case, it is useful to providean assistant to translate query or thesaurus. This kind of tools is based on a parallel concordancer we developed togenerate automatically parallel texts.As there are more comparable texts [3] than true parallel texts, we want to try our contrastive indexing systemwith multilingual comparable texts (see: Table 2, “Example of token extraction with a bilingual parallel collec-tion,” on page 6). Are we able to find some  multilingual statistic relief using the Reduced Deviation method withmultilingual documents talking about the same subject but with no word-for-word translation ?5.0  Conclusion & Future WorksThe system combines the common developments in information retrieval and free text indexing with the recentadvances in lexical statistic. The open architecture makes it easier to integrate the CFTI system to other applica-tions such as web server, digital library, multimedia databases and document delivery. The handling of structuredand unstructured data types (with the use of the SGML standart) provides a platform for indexing and linking doc-uments and multimedia objects.The following improvements could be accomplished with a modification of the CFTI system:• lemmatization and parsing to work with word-type rather than word-token,• multiple terms, compound names and NPs,TABLE 2. Example of token extraction with a bilingual parallel collectionl’abbaye de saint-savin (French) with word Cyprienthe abbey of saint-savin (English)with word CyprianRD Tokens F. Text F. Win RD Tokens F. Text F. Win11.1239 savin 128 17 11.7426 st 180 199.10967 saints 35 7 10.0558 savin 133 145.96213 frères 15 3 8.38392 saints 27 54.37415 probablement 12 2 8.04809 brothers 11 33.98536 vie 14 2 4.5643 life 14 23.81915 présence 15 2 4.2158 probably 16 23.66779 homme 16 2 3.92405 registers 18 23.52909 épisodes 17 2 3.92405 abbot 18 23.40129 registres 18 2 3.56263 episodes 21 23.17293 autel 20 2 3.35941 altar 23 2A Contrastive Indexing Method and its Integration in Aquarelle Folders28 March 1997 7• communication with the thesaurus browser (not only cut and paste).To test the system, we also planned to evaluate the prototype by the users. The criteria  used for evaluating shouldbe:• The user-friendly use and access,• The statistical parameters and thresholds,• Utilisation of a lemmatizer, a stemmer or without any (loss of the information granularity),• The coverage of the collection, the relevant matter,• The quantify, the quality of retrieved folders,• The relevance, precision and recall for retrieval.6.0  References[1] P. Bonhomme and L. Romary. Managing multilingual texts for educational purpose. InActes des Quinziè-mes Journées Internationales IA 95, Montpellier, 1995. EC2 & Cie.[2] P. Bonhomme and L. Romary. Design and integration of full-text indexing in aqaurelle folders. DeliverableD6.3, INRIA, 1997. Aquarelle Project.[3] C. Peters and E. Picchi. Cross language information retrieval: A system for comparable corpus querying. InWorkshop on Croo-linguistic Information Retrieval, pages 24–33. SIGIR’96, 1996.[4] R. Richardson, A. Smeaton, and J. Murphy. Using wordnet as a knowledge base for measuring semantic si-milarity between words. InProceedings of AICS Conference. Trinity College, Dublin, September 1994.[5] L. Romary, N. Mehl, and D. Woolls. The lingua parallel concordancing project. In L. Burnard, editor,Elec-tronic Texts and the Text Encoding Initiative, A Special Issue of TEXT Technology, pages 206–220. OxfordUniversity, 1995.[6] C. Scholl, C. Schmuck, A. Risk, A. Michard, and J. Pascon. Aquarelle annex i - project programme. IE-2005 Information Engineering, 1995.

A Contrastive Indexing Method and its Integration in Aquarelle Folders

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