Search results for "Word-sense disambiguation"

showing 5 items of 5 documents

Defining classifier regions for WSD ensembles using word space features

2006

Based on recent evaluation of word sense disambiguation (WSD) systems [10], disambiguation methods have reached a standstill. In [10] we showed that it is possible to predict the best system for target word using word features and that using this 'optimal ensembling method' more accurate WSD ensembles can be built (3-5% over Senseval state of the art systems with the same amount of possible potential remaining). In the interest of developing if more accurate ensembles, w e here define the strong regions for three popular and effective classifiers used for WSD task (Naive Bayes – NB, Support Vector Machine – SVM, Decision Rules – D) using word features (word grain, amount of positive and neg…

0303 health sciencesProbability learningWord-sense disambiguationComputer sciencebusiness.industryPattern recognition02 engineering and technologyDecision ruleSupport vector machine03 medical and health sciencesNaive Bayes classifier0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingStatistical analysisArtificial intelligencePolysemybusinessClassifier (UML)030304 developmental biology
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Building an Optimal WSD Ensemble Using Per-Word Selection of Best System

2006

In Senseval workshops for evaluating WSD systems [1,4,9], no one system or system type (classifier algorithm, type of system ensemble, extracted feature set, lexical knowledge source etc.) has been discovered that resolves all ambiguous words into their senses in a superior way. This paper presents a novel method for selecting the best system for target word based on readily available word features (number of senses, average amount of training per sense, dominant sense ratio). Applied to Senseval-3 and Senseval-2 English lexical sample state-of-art systems, a net gain of approximately 2.5 – 5.0% (respectively) in average precision per word over the best base system is achieved. The method c…

0303 health sciencesWord-sense disambiguationComputer scienceSample (material)Speech recognition02 engineering and technologyBase (topology)SemanticsSupport vector machine03 medical and health sciencesPattern recognition (psychology)Classifier (linguistics)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingWord (computer architecture)030304 developmental biology
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Interpretability in Word Sense Disambiguation using Tsetlin Machine

2021

Word-sense disambiguationComputer sciencebusiness.industryArtificial intelligencecomputer.software_genrebusinesscomputerNatural language processingInterpretabilityProceedings of the 13th International Conference on Agents and Artificial Intelligence
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SisHiTra : A Hybrid Machine Translation System from Spanish to Catalan

2004

In the current European scenario, characterized by the coexistence of communities writing and speaking a great variety of languages, machine translation has become a technology of capital importance. In areas of Spain and of other countries, coofficiality of several languages implies producing several versions of public information. Machine translation between all the languages of the Iberian Peninsula and from them into English will allow for a better integration of Iberian linguistic communities among them and inside Europe. The purpose of this paper is to show a machine translation system from Spanish to Catalan that deals with text input. In our approach, both deductive (linguistic) and…

Word-sense disambiguationMachine translationComputer sciencebusiness.industryAutomatic translationWord error rateHybrid machine translationcomputer.software_genreVariety (linguistics)language.human_languagelanguageCatalanArtificial intelligencebusinesscomputerNatural languageNatural language processing
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A Contrastive Evaluation of Word Sense Disambiguation Systems for Finnish

2019

Aiempi saneiden alamerkitysten yksiselitteistämistä käsittelevä työ, kuten monet muut luonnollisen kielen käsittelyyn liittyvät tehtävät, on enimmäkseen keskittynyt englannin kieleen. Vaikka hieman työtä on tehty myös muilla kielillä, mukaan lukien uralilaiset kielet, vertailevaa arviointia suomen kielen saneiden alamerkitysten yksiselitteistämisestä ei ole tähän mennessä julkaistu huolimatta siitä, että tarvittavat leksikaaliset resurssit, erityisesti FinnWordNet, ovat jo pitkään olleet saatavilla. Tämä työ pyrkii korjaamaan tilanteen. Se tarjoaa tuloksia merkittävimpiä lähestymistapoja saneiden alamerkitysten yksiselitteistämiseen edustavista ohjelmista, sisältäen joitakin parhaiten engla…

kieli ja kieletWord-sense disambiguationsuomen kieliComputer sciencebusiness.industrysanastotcomputer.software_genretietokonelingvistiikkaluonnollinen kieliuralilaiset kieletArtificial intelligencebusinesscomputerNatural language processingProceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages
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