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Practice of Word Sense Disambiguation
2018The paper aims at the community of researchers and practitioners that work in the area of natural language processing but do not specialize in the word sense disambiguation (WSD). It contains a brief introduction into WSD and describes the classical approaches to solve the problem.
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Margin perceptron for word sense disambiguation
Proceedings of the 2010 Symposium on Information and Communication Technology - SoICT '10, 2010Word Sense Disambiguation (WSD) is an AI-complete problem where senses of words in the documents must be correctly selected from a senses inventory. Support Vector Machines (SVM) method has been successfully applied to supervised WSD. In contrast, perceptron has not been popular in supervised WSD.
Kiem-Hieu Nguyen, Cheol-Young Ock
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Word sense disambiguation by semantic inference
2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC), 2017This paper proposes an algorithm for unsupervised Word Sense Disambiguation to bypass the knowledge bottleneck faced by supervised approaches. By simulating the semantic inference process performed by human language users, the algorithm makes use of a thesaurus to obtain potential substitute words for the target word in a sentence, builds substitute ...
Xinda Wang 0003 +3 more
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1988
Computational lexical approaches to disambiguation divide into syntactic category assignment such as whether farm is a noun or a verb (Milne, 1986) and word sense disambiguation within syntactic category.9 The latter problem is the subject of this chapter.
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Computational lexical approaches to disambiguation divide into syntactic category assignment such as whether farm is a noun or a verb (Milne, 1986) and word sense disambiguation within syntactic category.9 The latter problem is the subject of this chapter.
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Combining Classifiers for word sense disambiguation
Natural Language Engineering, 2002Classifier combination is an effective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier combination approaches for the word sense disambiguation task, studied over a diverse classifier pool which includes feature-enhanced Naïve Bayes, Cosine ...
Radu Florian +3 more
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Word2vec for Arabic Word Sense Disambiguation
2018Word embedding, where words are represented as vectors in a continuous space, has recently attracted much attention in natural language processing tasks due to their ability to capture semantic and syntactic relations between words from a huge amount of text.
Rim Laatar +2 more
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Word Sense Disambiguation with Semantic Networks
2008Word sense disambiguation (WSD) methods evolve towards exploring all of the available semantic information that word thesauri provide. In this scope, the use of semantic graphs and new measures of semantic relatedness may offer better WSD solutions. In this paper we propose a new measure of semantic relatedness between any pair of terms for the English
George Tsatsaronis 0001 +2 more
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Word sense disambiguation based on context selection using knowledge-based word similarity
Information Processing and Management, 2021Youngjoong Ko, Sunjae Kwon, Dongsuk Oh
exaly
Word Sense Disambiguation for Semantic Applications.
2006Natural language processing (NLP) has become the most significant obstacle that has been restricting the applications via the web. Today, very little of the content on the web can be understood by the machines, although vast amount of electronic information has been kept on them.
Orhan Z., Altan Z.
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