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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.
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 ...
By categorizing different words by certain elements, young children make sense of words and patterns within words. Word sorts combine both constructivist learning and teacher-directed instruction. [1] Students receive a stack of cards containing either pictures or words that have several types of contrasting sounds, patterns, or meanings.
One problem with word sense disambiguation is deciding what the senses are, as different dictionaries and thesauruses will provide different divisions of words into senses. Some researchers have suggested choosing a particular dictionary, and using its set of senses to deal with this issue.
A U.S. Marine helps a student with reading comprehension as part of a Partnership in Education program sponsored by Park Street Elementary School and Navy/Marine Corps Reserve Center Atlanta. The program is a community outreach program for sailors and Marines to visit the school and help students with class work.
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 ...
Athletes doing "Trump dance" celebrations should think about what, exactly, they're glorifying.
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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