16 research outputs found
SARIPOD : SystĂšme multi-Agent de Recherche Intelligente POssibiliste de Documents Web
La prĂ©sente thĂšse de doctorat en informatique propose un modĂšle pour une recherche d'information intelligente possibiliste des documents Web et son implĂ©mentation. Ce modĂšle est Ă base de deux RĂ©seaux Petits Mondes HiĂ©rarchiques (RPMH) et d'un RĂ©seau Possibiliste (RP) : Le premier RPMH consiste Ă structurer les documents retrouvĂ©s en zones denses de pages Web thĂ©matiquement liĂ©es les unes aux autres. Nous faisons ainsi apparaĂźtre des nuages denses de pages qui traitent d'un sujet et des sujets connexes (assez similaires sĂ©mantiquement) et qui rĂ©pondent toutes fortement Ă une requĂȘte. Le second RPMH est celui qui consiste Ă ne pas prendre les mots-clĂ©s tels quels mais Ă considĂ©rer une requĂȘte comme multiple en ce sens qu'on ne cherche pas seulement le mot-clĂ© dans les pages Web mais aussi les substantifs qui lui sont sĂ©mantiquement proches. Les RĂ©seaux Possibilistes combinent les deux RPMH afin d'organiser les documents recherchĂ©s selon les prĂ©fĂ©rences de l'utilisateur. En effet, l'originalitĂ© du modĂšle proposĂ© se dĂ©cline selon les trois volets suivants qui synthĂ©tisent nos contributions. Le premier volet s'intĂ©resse au processus itĂ©ratif de la reformulation sĂ©mantique de requĂȘtes. Cette technique est Ă base de relations de dĂ©pendance entre les termes de la requĂȘte. Nous Ă©valuons notamment les proximitĂ©s des mots du dictionnaire français « Le Grand Robert » par rapport aux termes de la requĂȘte. Ces proximitĂ©s sont calculĂ©es par le biais de notre approche de recherche des composantes de sens dans un RPMH de dictionnaire de mots par application d'une mĂ©thode basĂ©e sur le dĂ©nombrement des circuits dans le rĂ©seau. En fait, l'utilisateur du systĂšme proposĂ© choisit le nombre de mots sĂ©mantiquement proches qu'il dĂ©sire ajouter Ă chaque terme de sa requĂȘte originelle pour construire sa requĂȘte reformulĂ©e sĂ©mantiquement. Cette derniĂšre reprĂ©sente la premiĂšre partie de son profil qu'il propose au systĂšme. La seconde partie de son profil est constituĂ©e des choix des coefficients de pertinence possibilistes affectĂ©s aux entitĂ©s logiques des documents de la collection. Ainsi, notre systĂšme tient compte des profils dynamiques des utilisateurs au fur et Ă mesure que ces derniers utilisent le systĂšme. Ce dernier est caractĂ©risĂ© par son intelligence, son adaptativitĂ©, sa flexibilitĂ© et sa dynamicitĂ©. Le second volet consiste Ă proposer des relations de dĂ©pendance entre les documents recherchĂ©s dans un cadre ordinal. Ces relations de dĂ©pendance entre ces documents traduisent les liens sĂ©mantiques ou statistiques Ă©valuant les distributions des termes communs Ă des paires ou ensembles de documents. Afin de quantifier ces relations, nous nous sommes basĂ©s sur les calculs des proximitĂ©s entres ces documents par application d'une mĂ©thode de dĂ©nombrement de circuits dans le RPMH de pages Web. En effet, les documents peuvent ainsi ĂȘtre regroupĂ©s dans des classes communes (groupes de documents thĂ©matiquement proches). Le troisiĂšme volet concerne la dĂ©finition des relations de dĂ©pendance, entre les termes de la requĂȘte et les documents recherchĂ©s, dans un cadre qualitatif. Les valeurs affectĂ©es Ă ces relations traduisent des ordres partiels de prĂ©fĂ©rence. En fait, la thĂ©orie des possibilitĂ©s offre deux cadres de travail : le cadre qualitatif ou ordinal et le cadre quantitatif. Nous avons proposĂ© notre modĂšle dans un cadre ordinal. Ainsi, des prĂ©fĂ©rences entre les termes de la requĂȘte se sont ajoutĂ©es Ă notre modĂšle de base. Ces prĂ©fĂ©rences permettent de restituer des documents classĂ©s par prĂ©fĂ©rence de pertinence. Nous avons mesurĂ© aussi l'apport de ces facteurs de prĂ©fĂ©rence dans l'augmentation des scores de pertinence des documents contenant ces termes dans le but de pĂ©naliser les scores de pertinence des documents ne les contenant pas. Pour la mise en place de ce modĂšle nous avons choisi les systĂšmes multi-agents. L'avantage de l'architecture que nous proposons est qu'elle offre un cadre pour une collaboration entre les diffĂ©rents acteurs et la mise en Ćuvre de toutes les fonctionnalitĂ©s du systĂšme de recherche d'information (SRI). L'architecture s'accorde parfaitement avec le caractĂšre intelligent possibiliste et permet de bĂ©nĂ©ficier des capacitĂ©s de synergie inhĂ©rente entre les diffĂ©rentes composantes du modĂšle proposĂ©. Dans le prĂ©sent travail, nous avons donc pu mettre en exergue Ă travers les expĂ©rimentations effectuĂ©es l'intĂ©rĂȘt de faire combiner les deux RPMH via un rĂ©seau possibiliste dans un SRI, ce qui permet d'enrichir le niveau d'exploration d'une collection. Ce dernier n'est pas limitĂ© aux documents mais l'Ă©tend en considĂ©rant les requĂȘtes. En effet, la phase de reformulation sĂ©mantique de requĂȘte permet Ă l'utilisateur de profiter des autres documents correspondants aux termes sĂ©mantiquement proches des termes de la requĂȘte originelle. Ces documents peuvent exister dans d'autres classes des thĂšmes. En consĂ©quence, une reclassification proposĂ©e par le systĂšme s'avĂšre pertinente afin d'adapter les rĂ©sultats d'une requĂȘte aux nouveaux besoins des utilisateurs. ABSTRACT : This Ph.D. thesis proposes a new model for a multiagent possibilistic Web information retrieval and its implementation. This model is based on two Hierarchical Small-Worlds (HSW) Networks and a Possibilistic Networks (PN): The first HSW consists in structuring the founded documents in dense zones of Web pages which strongly depend on each other. We thus reveal dense clouds of pages which "speak" more or less about the same subject and related subjects (semantically similar) and which all strongly answer user's query. The second HSW consists in considering the query as multiple in the sense that we don't seek only the keyword in the Web pages but also its semantically close substantives. The PN generates the mixing of these two HSW in order to organize the searched documents according to user's preferences. Indeed, the originality of the suggested model is declined according to three following shutters' which synthesize our contributions. The first shutter is interested in the iterative process of query semantic reformulation. This technique is based on relationship dependence between query's terms. We evaluate in particular the semantics proximities between the words of the French dictionary "Le Grand Robert" and query's terms. These proximities are calculated via our approach of research of the semantics components in the HSW of dictionary of words by application of our method of enumeration of circuits in the HSW of dictionary. In fact, the user of the suggested system chooses the number of close words that he desire to add to each word of his initial query to build his semantically reformulated query. This one represents the first part of user's profile which he proposes to the system. The second part of its profile makes up of its choices of the coefficients of relevance possibilistic of the logical entities of the documents of the collection. Thus, our system takes account of the dynamic profiles of its users progressively they use the system, which proves its intelligence, its adaptability, its flexibility and its dynamicity. The second shutter consists in proposing relationship dependence between documents of the collection within an ordinal framework. These relationships dependence between these documents represent the semantic or statistical links evaluating the distributions of the general terms to pairs or sets of documents. In order to quantify these relationships, we are based on the calculations of the proximities between these documents by application of a method enumerating of circuits in the HSW of Web pages. Indeed, the documents can thus be clustered in common classes (groups of close documents). The third shutter is related to the definition of the relationships dependence between query's terms and documents of the collection, within a qualitative framework. The assigned values to these relations translate preferably partial orders. In fact, possibilistic theory offers two working frameworks: the qualitative or ordinal framework and the numerical framework. We proposed our model within an ordinal framework. Thus, we add to our basic model preferences between query's terms. These preferences make it possible to restore documents classified by relevance's preference. We also measured the contribution of these preferably factors in the increase of the relevance's scores of documents containing these terms with an aim of penalizing the relevance's scores of the documents not containing them. For the installation of this model we chose multiagent systems. The advantage of the proposed architecture is that it offers a framework for collaboration between the various actors and the implementation of all the functionalities of the information retrieval system. Architecture agrees perfectly with the possibilistic intelligent character and makes it possible to profit from the capacities of inherent synergy in the suggested model. We thus could put forward, through the carried out experiments, the goal of combining the two HSW via a possibilistic network in an information retrieval system, which makes it possible to enrich the exploration level of a collection. This exploration is not only limited to the documents but it extends by considering also the query. Indeed, the semantic query reformulation phase makes it possible to benefit user from other documents which contain some close terms of the initial query. These documents can exist in other topics classes. Consequently, a reclassification suggested by the system proves its relevance in order to adapt query's results to new user's needs
Towards an intelligent possibilistic web information retrieval using multiagent system.
PURPOSE - The purpose of this paper is to make a scientific contribution to web information retrieval (IR). Design/methodolog y/approach â A multiagent system for web IR is proposed based on new technologies: Hierarchical Small-Worlds (HSW) and Possibilistic Networks (PN). This system is based on a possibilistic qualitative approach which extends the quantitative one. FINDINGS â The paper finds that the relevance of the order of documents changes while passing from a profile to another. Even if the selected terms tend to select the relevant document, these terms are not the most frequent of the document. This criterion shows the asset of the qualitative approach of the SARIPOD system in the selection of relevant documents. The insertion of the factors of preference between query terms in the calculations of the possibility and the necessity consists in increasing the scores of possibilistic relevance of the documents containing these terms with an aim of penalizing the scores of relevance of the documents not containing them. The penalization and the increase in the scores are proportional to the capacity of the terms to discriminate between the documents of the collection. RESEARCH LIMITATIONS/IMPLICATIONS â It is planned to extend the tests of the SARIPOD system to other grammatical categories, like refining the approach for the substantives by considering for example, the verbal occurrences in names definitions, etc. Also, it is planned to carry out finer measurements of the performances of SARIPOD system by extending the tests with other types of web documents. PRACTICAL IMPLICATIONS â The system can be useful to help research students find their relevant scientific papers. It must be located in the document server of any research laboratory. ORIGINALITY/VALUE â The paper presents SARIPOD, a new qualitative possibilistic model for web IR using multiagent syste
Organizing Contextual Knowledge for Arabic Text Disambiguation and Terminology Extraction.
Ontologies have an important role in knowledge organization and information retrieval. Domain ontologies are composed of concepts represented by domain relevant terms. Existing approaches of ontology construction make use of statistical and linguistic information to extract domain relevant terms. The quality and the quantity of this information influence the accuracy of terminologyextraction approaches and other steps in knowledge extraction and information retrieval. This paper proposes an approach forhandling domain relevant terms from Arabic non-diacriticised semi-structured corpora. In input, the structure of documentsis exploited to organize knowledge in a contextual graph, which is exploitedto extract relevant terms. This network contains simple and compound nouns handled by a morphosyntactic shallow parser. The noun phrases are evaluated in terms of termhood and unithood by means of possibilistic measures. We apply a qualitative approach, which weighs terms according to their positions in the structure of the document. In output, the extracted knowledge is organized as network modeling dependencies between terms, which can be exploited to infer semantic relations.We test our approach on three specific domain corpora. The goal of this evaluation is to check if our model for organizing and exploiting contextual knowledge will improve the accuracy of extraction of simple and compound nouns. We also investigate the role of compound nouns in improving information retrieval results
A Possibilistic Query Translation Approach for Cross-Language Information Retrieval
International audienceIn this paper, we explore several statistical methods to find solutions to the problem of query translation ambiguity. Indeed, we propose and compare a new possibilistic approach for query translation derived from a probabilistic one, by applying a classical probability-possibility transformation of probability distributions, which introduces a certain tolerance in the selection of word translations. Finally, the best words are selected based on a similarity measure. The experiments are performed on CLEF-2003 French-English CLIR collection, which allowed us to test the effectiveness of the possibilistic approach
A hybrid approach for arabic semantic relation extraction
Information retrieval applications are essential tools to manage the huge amount of information in the Web. Ontologies have great importance in these applications. The idea here is that several data belonging to a domain of interest are represented and related semantically in the ontology, which can help to navigate, manage and reuse these data. Despite of the growing need of ontology, only few works were interested in Arabic language. Indeed, arabic texts are highly ambiguous, especially when diacritics are absent. Besides, existent works does not cover all the types of se-mantic relations, which are useful to structure Arabic ontol-ogies. A lot of work has been done on cooccurrence- based techniques, which lead to over-generation. In this paper, we propose a new approach for Arabic se-mantic relation extraction. We use vocalized texts to reduce ambiguities and propose a new distributional approach for similarity calculus, which is compared to cooccurrence. We discuss our contribution through experimental results and propose some perspectives for future research
Saripod : an Intelligent Possibilistic Web Information Retrieval using Multiagent system
La prĂ©sente thĂšse de doctorat en informatique propose un modĂšle pour une recherche d'information intelligente possibiliste des documents Web et son implĂ©mentation. Ce modĂšle est Ă base de deux RĂ©seaux Petits Mondes HiĂ©rarchiques (RPMH) et d'un RĂ©seau Possibiliste (RP) : Le premier RPMH consiste Ă structurer les documents retrouvĂ©s en zones denses de pages Web thĂ©matiquement liĂ©es les unes aux autres. Nous faisons ainsi apparaĂźtre des nuages denses de pages qui traitent d'un sujet et des sujets connexes (assez similaires sĂ©mantiquement) et qui rĂ©pondent toutes fortement Ă une requĂȘte. Le second RPMH est celui qui consiste Ă ne pas prendre les mots-clĂ©s tels quels mais Ă considĂ©rer une requĂȘte comme multiple en ce sens qu'on ne cherche pas seulement le mot-clĂ© dans les pages Web mais aussi les substantifs qui lui sont sĂ©mantiquement proches. Les RĂ©seaux Possibilistes combinent les deux RPMH afin d'organiser les documents recherchĂ©s selon les prĂ©fĂ©rences de l'utilisateur. En effet, l'originalitĂ© du modĂšle proposĂ© se dĂ©cline selon les trois volets suivants qui synthĂ©tisent nos contributions. Le premier volet s'intĂ©resse au processus itĂ©ratif de la reformulation sĂ©mantique de requĂȘtes. Cette technique est Ă base de relations de dĂ©pendance entre les termes de la requĂȘte. Nous Ă©valuons notamment les proximitĂ©s des mots du dictionnaire français « Le Grand Robert » par rapport aux termes de la requĂȘte. Ces proximitĂ©s sont calculĂ©es par le biais de notre approche de recherche des composantes de sens dans un RPMH de dictionnaire de mots par application d'une mĂ©thode basĂ©e sur le dĂ©nombrement des circuits dans le rĂ©seau. En fait, l'utilisateur du systĂšme proposĂ© choisit le nombre de mots sĂ©mantiquement proches qu'il dĂ©sire ajouter Ă chaque terme de sa requĂȘte originelle pour construire sa requĂȘte reformulĂ©e sĂ©mantiquement. Cette derniĂšre reprĂ©sente la premiĂšre partie de son profil qu'il propose au systĂšme. La seconde partie de son profil est constituĂ©e des choix des coefficients de pertinence possibilistes affectĂ©s aux entitĂ©s logiques des documents de la collection. Ainsi, notre systĂšme tient compte des profils dynamiques des utilisateurs au fur et Ă mesure que ces derniers utilisent le systĂšme. Ce dernier est caractĂ©risĂ© par son intelligence, son adaptativitĂ©, sa flexibilitĂ© et sa dynamicitĂ©. Le second volet consiste Ă proposer des relations de dĂ©pendance entre les documents recherchĂ©s dans un cadre ordinal. Ces relations de dĂ©pendance entre ces documents traduisent les liens sĂ©mantiques ou statistiques Ă©valuant les distributions des termes communs Ă des paires ou ensembles de documents. Afin de quantifier ces relations, nous nous sommes basĂ©s sur les calculs des proximitĂ©s entres ces documents par application d'une mĂ©thode de dĂ©nombrement de circuits dans le RPMH de pages Web. En effet, les documents peuvent ainsi ĂȘtre regroupĂ©s dans des classes communes (groupes de documents thĂ©matiquement proches). Le troisiĂšme volet concerne la dĂ©finition des relations de dĂ©pendance, entre les termes de la requĂȘte et les documents recherchĂ©s, dans un cadre qualitatif. Les valeurs affectĂ©es Ă ces relations traduisent des ordres partiels de prĂ©fĂ©rence. En fait, la thĂ©orie des possibilitĂ©s offre deux cadres de travail : le cadre qualitatif ou ordinal et le cadre quantitatif. Nous avons proposĂ© notre modĂšle dans un cadre ordinal. Ainsi, des prĂ©fĂ©rences entre les termes de la requĂȘte se sont ajoutĂ©es Ă notre modĂšle de base. Ces prĂ©fĂ©rences permettent de restituer des documents classĂ©s par prĂ©fĂ©rence de pertinence. Nous avons mesurĂ© aussi l'apport de ces facteurs de prĂ©fĂ©rence dans l'augmentation des scores de pertinence des documents contenant ces termes dans le but de pĂ©naliser les scores de pertinence des documents ne les contenant pas. Pour la mise en place de ce modĂšle nous avons choisi les systĂšmes multi-agents. L'avantage de l'architecture que nous proposons est qu'elle offre un cadre pour une collaboration entre les diffĂ©rents acteurs et la mise en Ćuvre de toutes les fonctionnalitĂ©s du systĂšme de recherche d'information (SRI). L'architecture s'accorde parfaitement avec le caractĂšre intelligent possibiliste et permet de bĂ©nĂ©ficier des capacitĂ©s de synergie inhĂ©rente entre les diffĂ©rentes composantes du modĂšle proposĂ©. Dans le prĂ©sent travail, nous avons donc pu mettre en exergue Ă travers les expĂ©rimentations effectuĂ©es l'intĂ©rĂȘt de faire combiner les deux RPMH via un rĂ©seau possibiliste dans un SRI, ce qui permet d'enrichir le niveau d'exploration d'une collection. Ce dernier n'est pas limitĂ© aux documents mais l'Ă©tend en considĂ©rant les requĂȘtes. En effet, la phase de reformulation sĂ©mantique de requĂȘte permet Ă l'utilisateur de profiter des autres documents correspondants aux termes sĂ©mantiquement proches des termes de la requĂȘte originelle. Ces documents peuvent exister dans d'autres classes des thĂšmes. En consĂ©quence, une reclassification proposĂ©e par le systĂšme s'avĂšre pertinente afin d'adapter les rĂ©sultats d'une requĂȘte aux nouveaux besoins des utilisateurs.This Ph.D. thesis proposes a new model for a multiagent possibilistic Web information retrieval and its implementation. This model is based on two Hierarchical Small-Worlds (HSW) Networks and a Possibilistic Networks (PN): The first HSW consists in structuring the founded documents in dense zones of Web pages which strongly depend on each other. We thus reveal dense clouds of pages which "speak" more or less about the same subject and related subjects (semantically similar) and which all strongly answer user's query. The second HSW consists in considering the query as multiple in the sense that we don't seek only the keyword in the Web pages but also its semantically close substantives. The PN generates the mixing of these two HSW in order to organize the searched documents according to user's preferences. Indeed, the originality of the suggested model is declined according to three following shutters' which synthesize our contributions. The first shutter is interested in the iterative process of query semantic reformulation. This technique is based on relationship dependence between query's terms. We evaluate in particular the semantics proximities between the words of the French dictionary "Le Grand Robert" and query's terms. These proximities are calculated via our approach of research of the semantics components in the HSW of dictionary of words by application of our method of enumeration of circuits in the HSW of dictionary. In fact, the user of the suggested system chooses the number of close words that he desire to add to each word of his initial query to build his semantically reformulated query. This one represents the first part of user's profile which he proposes to the system. The second part of its profile makes up of its choices of the coefficients of relevance possibilistic of the logical entities of the documents of the collection. Thus, our system takes account of the dynamic profiles of its users progressively they use the system, which proves its intelligence, its adaptability, its flexibility and its dynamicity. The second shutter consists in proposing relationship dependence between documents of the collection within an ordinal framework. These relationships dependence between these documents represent the semantic or statistical links evaluating the distributions of the general terms to pairs or sets of documents. In order to quantify these relationships, we are based on the calculations of the proximities between these documents by application of a method enumerating of circuits in the HSW of Web pages. Indeed, the documents can thus be clustered in common classes (groups of close documents). The third shutter is related to the definition of the relationships dependence between query's terms and documents of the collection, within a qualitative framework. The assigned values to these relations translate preferably partial orders. In fact, possibilistic theory offers two working frameworks: the qualitative or ordinal framework and the numerical framework. We proposed our model within an ordinal framework. Thus, we add to our basic model preferences between query's terms. These preferences make it possible to restore documents classified by relevance's preference. We also measured the contribution of these preferably factors in the increase of the relevance's scores of documents containing these terms with an aim of penalizing the relevance's scores of the documents not containing them. For the installation of this model we chose multiagent systems. The advantage of the proposed architecture is that it offers a framework for collaboration between the various actors and the implementation of all the functionalities of the information retrieval system. Architecture agrees perfectly with the possibilistic intelligent character and makes it possible to profit from the capacities of inherent synergy in the suggested model. We thus could put forward, through the carried out experiments, the goal of combining the two HSW via a possibilistic network in an information retrieval system, which makes it possible to enrich the exploration level of a collection. This exploration is not only limited to the documents but it extends by considering also the query. Indeed, the semantic query reformulation phase makes it possible to benefit user from other documents which contain some close terms of the initial query. These documents can exist in other topics classes. Consequently, a reclassification suggested by the system proves its relevance in order to adapt query's results to new user's needs
Recherche d'Information Possibiliste: De la DĂ©sambiguĂŻsation et la Reformulation de RequĂȘtes vers la FiabilitĂ© de l'Information RecherchĂ©e
Arabic text classification based on analogical proportions
Text classification is the process of labelling a given set of text documents with predefined classes or categories. Existing Arabic text classifiers are either applying classic Machine Learning algorithms such as k-NN and SVM or using modern deep learning techniques. The former are assessed using small text collections and their accuracy is still subject to improvement while the latter are efficient in classifying big data collections and show limited effectiveness in classifying small corpora with a large number of categories. This paper proposes a new approach to Arabic text classification to treat small and large data collections while improving the classification rates of existing classifiers. We first demonstrate the ability of analogical proportions (AP) (statements of the form âx is to as is to â), which have recently been shown to be effective in classifying âstructuredâ data, to classify âunstructuredâ text documents requiring preprocessing. We design an analogical model to express the relationship between text documents and their real categories. Next, based on this principle, we develop two new analogical Arabic text classifiers. These rely on the idea that the category of a new document can be predicted from the categories of three others, in the training set, in case the four documents build together a âvalidâ analogical proportion on all or on a large number of components extracted from each of them. The two proposed classifiers (denoted AATC1 and AATC2) differ mainly in terms of the keywords extracted for classification. To evaluate the proposed classifiers, we perform an extensive experimental study using five benchmark Arabic text collections with small or large sizes, namely ANT (Arabic News Texts) v2.1 and v1.1, BBC-Arabic, CNN-Arabic and AlKhaleej-2004. We also compare analogical classifiers with both classical ML-based and Deep Learning-based classifiers. Results show that AATC2 has the best average accuracy (78.78%) over all other classifiers and the best average precision (0.77) ranked first followed by AATC1 (0.73), NB (0.73) and SVM (0.72) for the ANT corpus v2.1. Besides, AATC1 shows the best average precisions (0.88) and (0.92), respectively for the BBC-Arabic corpus and AlKhaleej-2004, and the best average accuracy (85.64%) for CNN-Arabic over all other classifiers. Results demonstrate the utility of analogical proportions for text classification. In particular, the proposed analogical classifiers are shown to significantly outperform a number of existing Arabic classifiers, and in many cases, compare favourably to the robust SVM classifier
Improving Query Expansion by Automatic Query Disambiguation in Intelligent Information Retrieval
International audienceWe study in this paper the impact of WordSense Disambiguation (WSD) on Query Expansion (QE) for monolingual intelligent information retrieval. The proposed approaches for WSD and QE are based on corpus analysis using co-occurrence graphs modelled by possibilistic networks. Indeed, our model for relevance judgment uses possibility theory to take advantages of a double measure (possibility and necessity). Our experiments are performed using the standard ROMANSEVAL test collection for the WSD task and the CLEF-2003 benchmark for the QE process in French monolingual Information Retrieval (IR) evaluation. The results show the positive impact of WSD on QE based on the recall/precision standard metrics