A Comparative Study of Open-Domain and Specific-Domain Word Sense Disambiguation Based on Quranic Information Retrieval

Information retrieval is the process of analysing typed query as well as to retrieve relevant document according to the user query. Several issues can significantly affect the effectiveness of information retrieval. One of the common issue is the ambiguity lies on the words where a single word could yield several meanings. The process of identifying the exact sense of word is called Word Sense Disambiguation (WSD). Quran is the holly book for nearly 1.5 billion Muslims around the world. In particularly, Quran contains numerous words that can undergone multiple meanings. Therefore, there is a vital demand to apply WSD approach on Quran, in order, to improve the information retrieval. Several WSD approaches have been proposed for Quranic retrieval. However, these approaches are divided into two main categories; open-domain WSD approach and specific-domain WSD approach. Open-domain WSD is an approach that utilizes an open-domain dictionary such as WordNet, that is exploited to provide the exact sense. Whereas, domain-specific WSD approach aims to utilize a restricted training data that contain specific senses related to the domain of Quran. Hence, this study aims to establish a comparative study to investigate the two WSD categories including domain-specific and open-domain. For the domain-specific approach, a predefined example data has been collected to train Yarwosky algorithm which is a semisupervised machine learning technique. Then, based on the training, such algorithm can classify the exact sense for the words. In contrast, WordNet which is an open-domain dictionary has been used in this study with semantic distances, in order, to identify the similarity between the query word and the results of WordNet’s concepts. That dataset that has been used in this study is a Quranic translation. The experimental results have shown the mixed superiority of Yarwosky algorithm and WordNet WSD approach

Knowledge-based biomedical word sense disambiguation: comparison of approaches

<p>Abstract</p> <p>Background</p> <p>Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be used to annotate the biomedical literature. Statistical learning approaches have produced good results, but the size of the UMLS makes the production of training data infeasible to cover all the domain.</p> <p>Methods</p> <p>We present research on existing WSD approaches based on knowledge bases, which complement the studies performed on statistical learning. We compare four approaches which rely on the UMLS Metathesaurus as the source of knowledge. The first approach compares the overlap of the context of the ambiguous word to the candidate senses based on a representation built out of the definitions, synonyms and related terms. The second approach collects training data for each of the candidate senses to perform WSD based on queries built using monosemous synonyms and related terms. These queries are used to retrieve MEDLINE citations. Then, a machine learning approach is trained on this corpus. The third approach is a graph-based method which exploits the structure of the Metathesaurus network of relations to perform unsupervised WSD. This approach ranks nodes in the graph according to their relative structural importance. The last approach uses the semantic types assigned to the concepts in the Metathesaurus to perform WSD. The context of the ambiguous word and semantic types of the candidate concepts are mapped to Journal Descriptors. These mappings are compared to decide among the candidate concepts. Results are provided estimating accuracy of the different methods on the WSD test collection available from the NLM.</p> <p>Conclusions</p> <p>We have found that the last approach achieves better results compared to the other methods. The graph-based approach, using the structure of the Metathesaurus network to estimate the relevance of the Metathesaurus concepts, does not perform well compared to the first two methods. In addition, the combination of methods improves the performance over the individual approaches. On the other hand, the performance is still below statistical learning trained on manually produced data and below the maximum frequency sense baseline. Finally, we propose several directions to improve the existing methods and to improve the Metathesaurus to be more effective in WSD.</p

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

An analysis and comparison of predominant word sense disambiguation algorithms

This thesis investigates research performed in the area of natural language processing. It is the aim of this research to compare a selection of predominant word sense disambiguation algorithms, and also determine if they can be optimised by small changes to the parameters used by the algorithms. To perform this research, several word sense disambiguation algorithms will be implemented in Java, and run on a range of test corpora. The algorithms will be judged on metrics such as speed and accuracy, and any other results obtained; while an algorithm may be fast and accurate, there may be other factors making it less desirable. Finally, to demonstrate the purpose and usefulness of using better algorithms, the algorithms will be used in conjunction with a real world application. Five algorithms were used in this research: The standard Lesk algorithm, the simplified Lesk algorithm, a Lesk algorithm variant using hypernyms, a Lesk algorithm variant using synonyms, and a baseline performance algorithm. While the baseline algorithm should have been less accurate than the other algorithms, testing found that it could disambiguate words more accurately than any of the other algorithms, seemingly because the baseline makes use of statistical data in WordNet, the machine readable dictionary used for testing; data unable to be used by the other algorithms. However, with a few modifications, the Simplified Lesk algorithm was able to reach performance just a few percent lower than that of the baseline algorithm. It is the aim of this research to apply word sense disambiguation to automatic concept mapping, to determine if more accurate algorithms are able to display noticeably better results in a real world application. It was found in testing, that the overall accuracy of the algorithm had little effect on the quality of concept maps produced, but rather depended on the text being examined

Evaluating Variants of the Lesk Approach for Disambiguating Words

This paper presents a detailed analysis of the factors determining the performance of Lesk-based word sense disambiguation methods. We conducted a series of experiments on the original Lesk algorithm, adapted to WORDNET, and on some variants. These methods were evaluated on the test corpus from SENSEVAL2, English All Words, and on excerpts from SEMCOR. We designed a fine grain analysis of the answers provided by each variant in order to better understand the algorithms than by the mere precision and recall figures. 1

Automated Translation with Interlingual Word Representations

In dit proefschrift onderzoeken we het gebruik vertaalsystemen die gebruiken maken van een transferfase met interlinguale representaties van woorden. Op deze manier benaderen we het probleem van de lexicale ambiguïteit in de automatische vertaalsystemen als twee afzonderlijke taken: het bepalen van woordbetekenis en lexicale selectie. Eerst worden de woorden in de brontaal op basis van hun betekenis gedesambigueerd, resulterend in interlinguale representaties van woorden. Vervolgens wordt een lexicale selectiemodule gebruikt die het meest geschikte woord in de doeltaal selecteert. We geven een gedetailleerde beschrijving van de ontwikkeling en evaluatie van vertaalsystemen voor Nederlands-Engels. Dit biedt een achtergrond voor de experimenten in het tweede en derde deel van dit proefschrift. Daarna beschrijven we een methode die de betekenis van woorden bepaalt. Deze is vergelijkbaar met het klassieke Lesk-algoritme, omdat het gebruik maakt van het idee dat gedeelde woorden tussen de context van een woord en zijn definitie informatie over de betekenis ervan verschaffen. Wij gebruiken echter, in plaats daarvan, woord- en betekenisvectoren om de overeenkomst te berekenen tussen de definitie van een betekenis en de context van een woord. We gebruiken onze methode bovendien voor het localiseren en -interpreteren van woordgrapjes.Ten slotte presenteren we een model voor lexicale keuze dat lemma's selecteert, gegeven de abstracte representaties van woorden. Dit doen we door de grammaticale bomen te converteren naar hidden Markov bomen. Op deze manier kan de optimale combinatie van lemmas en hun context berekend worden

Multilinguisation d'ontologies dans le cadre de la recherche d'information translingue dans des collections d'images accompagnées de textes spontanés

Le Web est une source proliférante d'objets multimédia, décrits dans différentes langues natu- relles. Afin d'utiliser les techniques du Web sémantique pour la recherche de tels objets (images, vidéos, etc.), nous proposons une méthode d'extraction de contenu dans des collections de textes multilingues, paramétrée par une ou plusieurs ontologies. Le processus d'extraction est utilisé pour indexer les objets multimédia à partir de leur contenu textuel, ainsi que pour construire des requêtes formelles à partir d'énoncés spontanés. Il est basé sur une annotation interlingue des textes, conservant les ambiguïtés de segmentation et la polysémie dans des graphes. Cette première étape permet l'utilisation de processus de désambiguïsation factorisés au niveau d'un lexique pivot (de lexèmes interlingues). Le passage d'une ontologie en paramètre du système se fait en l'alignant de façon automatique avec le lexique interlingue. Il est ainsi possible d'utiliser des ontologies qui n'ont pas été conçues pour une utilisation multilingue, et aussi d'ajouter ou d'étendre l'ensemble des langues et leurs couvertures lexicales sans modifier les ontologies. Un démonstrateur pour la recherche multilingue d'images, développé pour le projet ANR OMNIA, a permis de concrétiser les approches proposées. Le passage à l'échelle et la qualité des annotations produites ont ainsi pu être évalués.The World Wide Web is a proliferating source of multimedia objects described using various natural languages. In order to use semantic Web techniques for retrieval of such objects (images, videos, etc.), we propose a content extraction method in multilingual text collections, using one or several ontologies as parameters. The content extraction process is used on the one hand to index multimedia objects using their textual content, and on the other to build formal requests from spontaneous user requests. The process is based on an interlingual annotation of texts, keeping ambiguities (polysemy and segmentation) in graphs. This first step allows using common desambiguation processes at th elevel of a pivot langage (interlingual lexemes). Passing an ontology as a parameter of the system is done by aligning automatically its elements with the interlingual lexemes of the pivot language. It is thus possible to use ontologies that have not been built for a specific use in a multilingual context, and to extend the set of languages and their lexical coverages without modifying the ontologies. A demonstration software for multilingual image retrieval has been built with the proposed approach in the framework of the OMNIA ANR project, allowing to implement the proposed approaches. It has thus been possible to evaluate the scalability and quality of annotations produiced during the retrieval process.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF