AUTOMATIC ACQUISITION OF HYPONYMS FROM LARGE TEXT CORPORA PDF

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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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Automatic Acquisition of Hyponyms from Large Text Corpora

Two goals motivate the approach: The relation missed the needed information about the kind of species. Notify me of new comments via email. This information may have been contained in a previous sentence.

The base pattern that the researchers started with wasand they presented the five others shown below. 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.

Lastly, if one or both noun phrases were not in WordNet, then the words and their relation were suggested. Noun synsets are organized hierarchically by the hyponymy relation. Ahtomatic paper has 3, citations.

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Automatic acquisition and use of some of the knowledge in physics texts John Batali Showing of 21 references. If both words were in WordNet but the relation was not, then a new hyponym connection was suggested. BrentRobert C.

Find the commonalities among the locations and hypothesize patterns that indicate the relation of interest. 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.

Automatic Acquisition of Hyponyms from Large Text Corpora | Stephen Zakrewsky

Then repeat, starting at step 2. Find locations in the text corpus where these expressions occur near each other. 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.

Other types of relations were tried without success. Citations Publications citing this paper. They then employed a recursive technique to discover new patterns. This paper has highly influenced other papers.

You are commenting using your Facebook account. You are commenting using your WordPress. Good patterns almost always indicate the relation of interest, and they can be recognized with little or no pre-encoded knowledge.

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The paper presents a method for automatic acquisition of hyponymy relations from raw text.

CiteSeerX — Automatic Acquisition of Hyponyms from Large Text Corpora

The approach is based on pattern matching. See our FAQ for additional information. If both noun phrases identified were in WordNet and the hyponym was in the hierarchy, then the result was verified.

Showing of 2, extracted citations. Contributions The paper presents a method for automatic acquisition of hyponymy relations from raw text. CuttingJulian KupiecJan O. Choose a lexical relation that is of interest. Post was not sent – check your email addresses! Text corpus Automwtic for additional papers on this topic.

Reconciling information contained in separate sentences may be challenging with pattern recognition alone. Sorry, your blog cannot share posts by email.

Appositives were difficult to match accurately. For example, the was found where steatornis is a species of bird. Statistical approaches have also been used that look to determine lexical relations by looking at very large text samples.