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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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The Effect of Windowing in Word Sense Disambiguation
2005In this paper, the effect of different windowing schemes to the success rate of word sense disambiguation is probed. In these windowing schemes it is considered that the impact of a neighbor word to the correct sense of the target word should be somewhat related to it’s distance to the target word.
Ergin Altintas +2 more
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Word Sense Disambiguation with GermaNet
2015The subject of this dissertation is boosting research on word sense disambiguation (WSD) for German. WSD is a very active area of research in computational linguistics, but most of the work is focused on English. One of the factors that has hampered WSD research for other languages such as German is the lack of appropriate resources, particularly in ...
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Word Sense Disambiguation Based on Vicarious Words
2008 Fourth International Conference on Natural Computation, 2008This paper presents the concept of vicarious words and develops a new unsupervised Chinese word sense disambiguation method. This method, after statistical learning from the vicarious words, realizes unsupervised word sense disambiguation by calculating mutual information to measure the degree of collocation information between the ambiguous words and ...
Zhimao Lu, Dongmei Fan, Rubo Zhang
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2018
Word sense disambiguation (WSD) is the process of identifying the meanings of words in context. The difficulty of this problem stems from the subtlety of word sense differences and the need for some level of understanding. This chapter describes the main approaches to the problem, methods for evaluating performance, and potential applications.
Eneko Agirre, Mark Stevenson
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Word sense disambiguation (WSD) is the process of identifying the meanings of words in context. The difficulty of this problem stems from the subtlety of word sense differences and the need for some level of understanding. This chapter describes the main approaches to the problem, methods for evaluating performance, and potential applications.
Eneko Agirre, Mark Stevenson
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Graph and Word Similarity for Word Sense Disambiguation
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2020This paper proposes a graph and word similarity method for word sense disambiguation. Perform word sense mapping on the context words of a given ambiguous sentence to obtain the corresponding English words; compute the English word similarity of the obtained English words, and take the English words as vertices, the semantic relations between words as ...
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An improved approach to word sense disambiguation
2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2014Words in the English language often correspond to different meanings in different contexts. Such words are referred to as polysemous words i.e. words having more than one sense. This paper presents a knowledge based algorithm for disambiguating polysemous words using computational linguistics tool, WordNet.
Pradeep Sachdeva +2 more
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Domain kernels for word sense disambiguation
Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05, 2005In this paper we present a supervised Word Sense Disambiguation methodology, that exploits kernel methods to model sense distinctions. In particular a combination of kernel functions is adopted to estimate independently both syntagmatic and domain similarity.
Alfio Massimiliano Gliozzo +2 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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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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