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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.
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 ...
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 each discovered sense of a target word. [6]
Word problem from the Līlāvatī (12th century), with its English translation and solution. In science education, a word problem is a mathematical exercise (such as in a textbook, worksheet, or exam) where significant background information on the problem is presented in ordinary language rather than in mathematical notation.
Word-sense disambiguation is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition , it is usually subconscious.
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 ...
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