... target word, providing a decision criterionfor sense matching. In other words, we expect thatunder a matchingsense the target word would oc-cur in prototypical contexts of the source word. To ... sense matches the source word. On the other hand, a direct approach would address the binary sense matching task directly, without selecting explicitlya concrete sense for the target word. This sectiondescribes ... unsupervised indirect meth-ods for the sensematching task. Direct. Conceptually, the most appealing solu-tion for the sensematching task is the one-classapproach proposed for the direct method...
... classification-based approach to WordSense Disambigua-tion. Instead of using a predefined monolin-gual sense- inventory such as WordNet, we usea language-independent framework where the word senses are derived ... Cross-Lingual Word Sense Disambiguation task for all five targetlanguages.1 Introduction Word Sense Disambiguation (WSD) is the NLPtask that consists in selecting the correct sense ofa polysemous word ... testing phase. This system also differs from theUvt-WSD and ParaSense systems in the sense thatthe word senses are derived from WordNet, whereasthe other systems do not use any external resources.The...
... two words. An ambiguous word has the same number of EPs as of senses. Each EP's sense maps to a sense of ambiguous word. The semantic equivalence demands further equivalence at each sense ... monosemous word is usually synonymous to some polysemous words. For example the words "信守, 严守, 恪守遵照 遵从 遵循, , , , 遵守" has similar meaning as one of the senses of the ambiguous word ... morpheme words for every sense of the word in construction of the EP, rich semantic information can be acquired in the training step and is an advantage for sense disambiguation. 2) Senseval-3...
... describes SENSELEARNER – aminimally supervised wordsense disam-biguation system that attempts to disam-biguate all content words in a text usingWordNet senses. We evaluate the accu-racy of SENSELEARNER ... minimally supervised word- sense dis-ambiguation that attempts to disambiguate all contentwords in a text using WordNet senses. The results ob-tained on both SENSEVAL-2 and SENSEVAL-3 datasets ... a memory-based word- expert approach to unrestricted wordsense dis-ambiguation. In Proceedings of the ACL Workshop on WordSense Disambiguatuion: Recent Successes andFuture Directions”, Philadelphia,...
... the Senseval 2 Englishlexical sample task. It includes 73 target words,among which nouns, adjectives, adverbs and verbs.For each word, training and test instances taggedwith WordNet senses ... text analysis. In Proceedings ofTable 1: Two of the Senseval-2 sense classes for the target word “art”, from WordNet 1.7 (Fellbaum 1998).Class Sense 1 the creation of beautiful or significant things2 ... with sense class 1: training example with sense class 2: test example with unknown sense class: test example with predicted sense first principal“ quasi-axis”class 2 (correct sense...
... topic of this paper is wordsense induction, that is the automatic discovery of the possible senses of a word. A related problem is wordsense disambiguation: Here the senses are assumed to ... the overall be-havior of a word in a corpus. If a word is se-mantically ambiguous, this means that these vectors are mixtures of all its senses. Inducing a word s senses therefore involves ... Problem of Automatic WordSense Induction Reinhard Rapp University of Mainz, FASK D-76711 Germersheim, Germany rapp@mail.fask.uni-mainz.de Abstract Recent studies in wordsense induction...
... of the SENSEVAL-3. Barcelona.Niu, Zheng-Yu, Dong-Hong Ji, and Chew-Lim Tan. 2007.I2r: Three systems for wordsense discrimination, chinese word sense disambiguation, and english wordsense dis-ambiguation. ... sam-pling a sense from the sense distribution, thenchoosing a word from the sense- context distribu-tion. P(si= j) denotes the probability that the jth sense was sampled for the ith word token ... plausible senses fordrug on the WSJ corpus (top half of Table 1). Sense 1 corresponds to the “enforcement” sense of drug, Sense 2 refers to “medication”, Sense 3to the “drug industry” and Sense...
... each sense of a word to predict the predominant sense. As in our earlierwork (Chan and Ng, 2005b), we normalized theprevalence score of each sense to obtain estimated sense priors for each word, ... @comp.nus.edu.sgAbstractInstances of a word drawn from differentdomains may have different sense priors(the proportions of the different senses ofa word) . This in turn affects the accuracyof wordsense disambiguation ... difference in sense priors (i.e., the proportions of the differentsenses of a word) between BC and WSJ. For in-stance, the noun interest has these 6 senses in theDSO corpus: sense 1, 2, 3,...
... Re-latedness to Perform WordSense Disambiguation. Sinopalnikova, A. (2004). Word Association Thesau-rus as a Resource for Building Wordnet. In GWC 2004. Sussna, M. (1993). WordSense Disambiguation ... of WordNet), we still can’t com-pletely rely on WordNet, which focuses on the paradigmatic relations of words, to fully cover the complexity of contextual happenings of words. Since the word ... Then for each context word we retrieve its corresponding words in each word list and calculate the similarity be-tween the target and these words including the context words. As a result we...
... IntroductionMany words have multiple meanings, depending onthe context in which they are used. Wordsense dis-ambiguation (WSD) is the task of determining thecorrect meaning or sense of a word in context. ... n-gram matching, where n is up to 4.First, we performed word alignment on the FBISparallel corpus using GIZA++ (Och and Ney, 2000)in both directions. The word alignments of bothdirections ... Chinese-English word- based statistical MT system using theISI ReWrite decoder (Germann, 2003). Though theyacknowledged that directly using English transla-tions as word senses would be ideal,...
... words to the right and left of the verb, identified using POS tags, represented by has_narrow(snt, word_ position, word) : has_narrow(snt1, 1st _word_ left, mind). has_narrow(snt1, 1st _word_ right, ... the positions of the words, represented by has_narrow_trns(snt, word_ position, portuguese _word) : has_narrow_trns(snt1, 1st _word_ right, como). has_narrow_trns(snt1, 2nd _word_ right, um). … ... simple, containing only the sentence identifier and the sense of the verb in that sentence, i.e. sense( snt, sense) : sense( snt1,voltar). sense( snt2,ir). … Based on the examples, background...
... IntroductionIn natural language, a word often assumes differentmeanings, and the task of determining the correctmeaning, or sense, of a word in different contextsis known as wordsense disambiguation (WSD). ... the dif-ference in sense priors (i.e., the proportions of thedifferent senses of a word) between BC and WSJ.When the authors assumed they knew the sense pri-ors of each word in BC and WSJ, ... of the different senses of each word were the same between BC and WSJ. We can simi-larly choose BC examples such that the sense priorsin the BC training data adhere to the sense priors inthe...
... methodof wordsense disambiguation that com-putes the intended sense of a target word, using WordNet-based measures of seman-tic relatedness (Patwardhan et al., 2003).SenseRelate::TargetWord is ... byselecting that sense of the target word which is most related to the context words.Relatedness between word senses is mea-sured using the WordNet::Similarity Perlmodules.1 IntroductionMany words ... Relatedness MeasureContextTarget Sense PreprocessingFormat Filter Sense InventoryContext SelectionPostprocessingPick Sense Figure 1: A generalized framework for WordSense Disambiguation.modules...
... significance.L1 words that translate into the same L2 word are grouped into clusters;SALAAM identifies the appropriate senses forthe words in those clusters based on the wordssenses’ proximity in WordNet. ... GenCor(Mihalcea&Moldovan, 1999). GenCor createsseeds from monosemous words in WordNet, Sem-cor data, sense tagged examples from the glossesof polysemous words in WordNet, and other handtagged data if available. ... that directly and indirectly affect the perfor-mance of quantified as a performanceratio, PR. Sense Distribution Correlation (SDC) and Sense Context Confusability (SCC) have the highestdirect...
... sample task of SENSEVAL-2. We rely on two sources to decide on the sense classes of w: (i) The sense definitions in WordNet 1.7, which lists seven senses for the noun channel. Two senses are ... Hopkins SENSEVAL2 system descrip-tions. In Proceedings of the Second International Workshop on Evaluating WordSense Disambigua-tion Systems (SENSEVAL-2), pages 163-166. WordNet 1.7 sense ... defining sense distinction in terms of different tar-get translations, the outcome of wordsense disam-biguation of a source language word is the selection of a target word, which directly...