106 research outputs found
Sense Embeddings in Knowledge-Based Word Sense Disambiguation
International audienc
D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation
Word sense disambiguation (WSD) is a problem in the field of computational
linguistics given as finding the intended sense of a word (or a set of words)
when it is activated within a certain context. WSD was recently addressed as a
combinatorial optimization problem in which the goal is to find a sequence of
senses that maximize the semantic relatedness among the target words. In this
article, a novel algorithm for solving the WSD problem called D-Bees is
proposed which is inspired by bee colony optimization (BCO)where artificial bee
agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a
standard dataset (SemEval 2007 coarse-grained English all-words task corpus)and
is compared to simulated annealing, genetic algorithms, and two ant colony
optimization techniques (ACO). It will be observed that the BCO and ACO
approaches are on par
Word Sense Disambiguation using WSD specific Wordnet of Polysemy Words
This paper presents a new model of WordNet that is used to disambiguate the
correct sense of polysemy word based on the clue words. The related words for
each sense of a polysemy word as well as single sense word are referred to as
the clue words. The conventional WordNet organizes nouns, verbs, adjectives and
adverbs together into sets of synonyms called synsets each expressing a
different concept. In contrast to the structure of WordNet, we developed a new
model of WordNet that organizes the different senses of polysemy words as well
as the single sense words based on the clue words. These clue words for each
sense of a polysemy word as well as for single sense word are used to
disambiguate the correct meaning of the polysemy word in the given context
using knowledge based Word Sense Disambiguation (WSD) algorithms. The clue word
can be a noun, verb, adjective or adverb
Création de clusters sémantiques dans des familles morphologiques à partir du TLFi
National audienceBuilding lexical resources is a time-consuming and expensive task, mainly when it comes to morphological lexicons. Such resources describe in depth and explicitly the morphological organization of the lexicon, completed with semantic information to be used in NLP applications. The work we present here goes on such direction, and especially, on refining an existing resource with automatically acquired semantic information. Our goal is to semantically characterize morpho-phonological families (words sharing a same base form and semantic continuity). To this end, we have used data from the TLFi which has been morpho-syntactically annotated. The first results of such a task will be analyzed and discussed.La constitution de ressources linguistiques est une tâche longue et coûteuse. C'est notamment le cas pour les ressources morphologiques. Ces ressources décrivent de façon approfondie et explicite l'organisation morphologique du lexique complétée d'informations sémantiques exploitables dans le domaine du TAL. Le travail que nous présentons dans cet article s'inscrit dans cette perspective et, plus particulièrement, dans l'optique d'affiner une ressource existante en s'appuyant sur des informations sémantiques obtenues automatiquement. Notre objectif est de caractériser sémantiquement des familles morpho-phonologiques (des mots partageant une même racine et une continuité de sens). Pour ce faire, nous avons utilisé des informations extraites du TLFi annoté morpho-syntaxiquement. Les premiers résultats de ce travail seront analysés et discutés
Word Sense Disambiguation on English Translation of Holy Quran
This article proposes a system based on the interpretation on the Quranic text that has been translated into English language using word sense disambiguation. This system is based on a combination of three traditional semantic similarity measurements, which are Wu-Palmer (WUP), Lin (LIN), and Jiang-Conrath (JCN) for word sense disambiguation on the English Al-Quran. The experiment was performed to obtain the best overall similarity score. The empirical results demonstrate that the combination of the three mentioned semantic similarity techniques obtained competitive results when compared with using individual similarity measurements
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