AUTOMATIC ACQUISITION OF HYPONYMS FROM LARGE TEXT CORPORA PDF

AUTOMATIC ACQUISITION OF HYPONYMS FROM LARGE TEXT CORPORA PDF

Download Citation on ResearchGate | Automatic Acquisition of Hyponyms from Large Text Corpora | We describe a method for the automatic. Automatic Acquisition of Hyponyms from Large Text Corpora. Anthology: C ; Volume: COLING Volume 2: The 15th International Conference on. This post is a review of the paper: Hearst, Marti A. “Automatic acquisition of hyponyms from large text corpora. In Proceedings of the.

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This information may have been contained in a previous sentence. Automatic acquisition and use of some of the knowledge in physics texts John Batali They then employed a recursive technique to discover new patterns. One reason was due the type of data contained in WordNet, but it also was suggested in general that it is difficult to know which modifiers are important to the relation.

Citations Publications citing this paper.

The relation missed the needed information about the kind of species. Good patterns occur frequently and in many text genres. Noun synsets are organized hierarchically by the hyponymy relation. Text corpus Search for additional papers on this topic. This paper has highly influenced other papers.

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The approach described in this paper is different in that only one sample of a relation needs to be found in a text to be useful. Showing of 2, extracted citations. A common issue was underspecification. Post was not sent – check your email addresses!

It does not require parsing nor context specific, preencoded knowledge. Semantic Scholar estimates that this publication has 3, citations based on the available data. Topics Discussed in This Paper. If both words were in WordNet but the relation was not, then a new hyponym connection was suggested.

Automatic Acquisition of Hyponyms from Large Text Corpora

To find out more, including how to control cookies, see here: Statistical acquixition have also been used that look to determine lexical relations by looking at very large text samples. Two goals motivate the approach: Email required Address never made public.

The base pattern that the researchers started with wasand they presented the five others shown below. Find the commonalities among the locations and hypothesize patterns that indicate the relation of interest. Fill in your details below or click an icon to log in: Find locations in the text corpus where these expressions occur near each other.

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CiteSeerX — Automatic Acquisition of Hyponyms from Large Text Corpora

This paper looks at extracting information from raw text. Showing of 21 references. Lastly, if one or both noun phrases were not in WordNet, then the words and their relation were suggested. Leave a Reply Cancel reply Enter your comment here Contributions The paper presents a method for automatic acquisition of hyponymy relations from raw text. Other types of relations were tried without success.

We identify a set of lexicosyntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest.

You are commenting using your Twitter account. The researchers found the first pattern manually by looking over texts.