Discriminating Among Word Meanings by Identifying Similar Contexts
نویسندگان
چکیده
Word sense discrimination is an unsupervised clustering problem, which seeks to discover which instances of a word/s are used in the same meaning. This is done strictly based on information found in raw corpora, without using any sense tagged text or other existing knowledge sources. Our particular focus is to systematically compare the efficacy of a range of lexical features, context representations, and clustering algorithms when applied to this problem.
منابع مشابه
Word Sense Discovery and Disambiguation
The work is based on the assumption that words with similar syntactic usage have similar meaning, which was proposed by Zellig S. Harris (1954,1968). We study his assumption from two aspects: firstly, different meanings (word senses) of a word should manifest themselves in different usages (contexts), and secondly, similar usages (contexts) should lead to similar meanings (word senses). If we s...
متن کاملVörös , Zoltán Szabó and
Explaining unintelligible words is a practical problem for text obtained by optical character recognition, from the Web (e.g., because of misspellings), etc. Approaches to wikification, to enriching text by linking words to Wikipedia articles, could help solve this problem. However, existing methods for wikification assume that the text is correct, so they are not capable of wikifying erroneous...
متن کاملSemantics of haq in the Glorious Quran
Meaning plays a very important role at all levels of linguistic analysis and in linguistics. We can say that the word itself and out of the chain of speech doesn’t show the true meaning. It should be in relation with other signs within the language that its meaning be relived. Quran, the precious word of Allah, contains words that take a variety of meanings in the syntactic and topical con...
متن کاملIdentifying Similar Words and Contexts in Natural Language with SenseClusters
SenseClusters is a freely available intelligent system that clusters together similar contexts in natural language text. Thereafter it assigns identifying labels to these clusters based on their content. It is a purely unsupervised approach that is language independent, and uses no knowledge other than what is available in raw un-annotated corpora. In addition to clustering similar contexts, it...
متن کاملDecision Trees for Sense Disambiguation of Prepositions: Case of Over
This paper proposes two decision trees for determining the meanings of the prepositional uses of over by using the contextual information. It first examines the meanings of the prepositional uses of over and then aims at identifying the contexts for interpreting the meanings. Some contexts are complementary features, and that makes the decision trees simple. The trees have been tested on a corp...
متن کامل