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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You are commenting using your Facebook account. Gather terms for which this relation holds. It builds on the success of using pattern recognition for the task of information extraction.

Automatic Acquisition of Hyponyms from Large Text Corpora | UC Berkeley School of Information

The relation missed drom needed information about the kind of species. Good patterns occur frequently and in many text genres. Statistical approaches have also been used that look to determine lexical relations by looking at very large text samples. Showing of 2, extracted citations. You are commenting auto,atic your WordPress. The researchers found the first pattern manually by looking over texts. Reconciling information contained in separate sentences may be challenging with pattern recognition alone.

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It does not require parsing nor context specific, preencoded knowledge.

The approach is based on pattern matching. Choose a lexical relation that is of interest. For example, the was found where steatornis is a species of bird. Fill in your details below or click an icon to log in: Find the commonalities among the locations and hypothesize patterns that indicate the relation of interest.

Automatic Acquisition of Hyponyms from Large Text Corpora

Two goals motivate the approach: By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. Similarly, the relation can be understood by relaxing the ISA definition of hyponym to one of close semantic similarity. A common issue was underspecification. BrentRobert C. 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 corporra.

If both noun phrases identified were in WordNet and the hyponym was in the hierarchy, then the result was verified. This paper has 3, citations. For them, it was different subsets of the hyponym relation. You are commenting using your Twitter account.

Skip to search form Skip to main content. When comparing against WordNet, three outcomes were considered. They can be used to augment and verify existing lexicons. 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. Showing of 21 references. WordNet contains larbe, noun forms and 26, synsets.

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Lastly, if one or both noun phrases were not in WordNet, then the words and their relation were suggested. See our FAQ for additional information. From This Paper Figures, tables, and topics from this paper.

Automatic Acquisition of Hyponyms from Large Text Corpora | Stephen Zakrewsky

They can be used to learn semantics of tet noun phrases. Once a new pattern is discovered, use it to find more instances of the relation. Email required Address never made public.

Good patterns almost always indicate the relation of interest, and they can be recognized with little or no pre-encoded knowledge. Patterns The approach is based on pattern matching. Other types of relations were tried without success.