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Word clustering is a different approach to the induction of word senses. It consists of clustering words, which are semantically similar and can thus bear a specific meaning. Lin’s algorithm [ 5 ] is a prototypical example of word clustering, which is based on syntactic dependency statistics, which occur in a corpus to produce sets of words ...
for every sense of the word being disambiguated one should count the number of words that are in both the neighborhood of that word and in the dictionary definition of that sense; the sense that is to be chosen is the sense that has the largest number of this count. A frequently used example illustrating this algorithm is for the context "pine ...
In linguistics, a word sense is one of the meanings of a word. For example, a dictionary may have over 50 different senses of the word "play", each of these having a different meaning based on the context of the word's usage in a sentence, as follows: We went to see the play Romeo and Juliet at the theater.
The underlying assumption is that similar senses occur in similar contexts, and thus senses can be induced from text by clustering word occurrences using some measure of similarity of context, [27] a task referred to as word sense induction or discrimination. Then, new occurrences of the word can be classified into the closest induced clusters ...
In computational linguistics the Yarowsky algorithm is an unsupervised learning algorithm for word sense disambiguation that uses the "one sense per collocation" and the "one sense per discourse" properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and in a ...
WordNet is the most commonly used computational lexicon of English for word-sense disambiguation (WSD), a task aimed at assigning the context-appropriate meanings (i.e. synset members) to words in a text. [14] However, it has been argued that WordNet encodes sense distinctions that are too fine-grained.
Senseval-3 – included tasks for word sense disambiguation, as well as identification of semantic roles, multilingual annotations, logic forms, subcategorization acquisition. SemEval-2007 – included tasks which were more elaborate than Senseval as it crosses the different areas of studies in Natural Language Processing
Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing [2] and word-sense disambiguation. [3] Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or ...
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