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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 ...
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 ...
Lexical substitution is strictly related to word sense disambiguation (WSD), in that both aim to determine the meaning of a word. However, while WSD consists of automatically assigning the appropriate sense from a fixed sense inventory, lexical substitution does not impose any constraint on which substitute to choose as the best representative ...
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 today's puzzle, there are eight theme words to find (including the spangram). Hint: The first one can be found in the top-half of the board. Here are the first two letters for each word: DA. NO ...
Given the difficulties of identifying word senses, other tasks relevant to this topic include word-sense induction, subcategorization acquisition, and evaluation of lexical resources. The second major area in semantic analysis is the understanding of how different sentence and textual elements fit together.
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