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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From This Paper Figures, tables, and topics from this paper. Grolier Electronic Publishing, Danbury…. Citations Publications citing this paper. When comparing to WordNet, relations were restricted to only nouns without modifiers.

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. It does not require parsing nor context specific, preencoded knowledge. If both noun phrases identified were in WordNet and the hyponym was in the hierarchy, then the result was verified. Fill in your details below or click an icon to log in: It builds on the success of using pattern recognition for the task of information extraction.

One reason was due the type of data contained in WordNet, larbe it also was suggested in general that it is difficult to know which modifiers are important to the relation.

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BrentRobert C. References Publications referenced by this paper. By continuing to use this website, you agree to their use. WordNet contains 34, noun forms and 26, synsets. Notify me of new comments via email. Post was not sent – check your email addresses!

Automatic Acquisition of Hyponyms from Large Text Corpora

When comparing against WordNet, three outcomes were considered. Contributions The paper presents a method for automatic acquisition of hyponymy relations from raw text. Two goals motivate the approach: Find the commonalities among the locations and hypothesize patterns that hyponms the relation of interest. Semantic Scholar estimates that this publication has 3, citations based on the available data. Topics Discussed in This Paper. The base pattern that the researchers started with wasand they presented the five others shown below.

The paper presents a method for automatic acquisition of hyponymy relations from raw text. Leave a Reply Cancel reply Enter your comment here Find locations in the text corpus where these expressions occur near each other.

This paper looks at extracting information from raw text. Similarly, the relation can be understood by relaxing the ISA definition of hyponym to one of hyponyks semantic similarity. Reconciling hyponymw contained in separate sentences may be challenging with pattern recognition alone.

Appositives were difficult to match accurately. Corora of 21 references. Choose a lexical relation that is of interest. Noun synsets are organized hierarchically by the hyponymy relation. They can be used to learn semantics of familiar noun phrases. For them, it was different subsets of the hyponym relation. This paper has 3, citations.

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Automatic Acquisition of Hyponyms from Large Text Corpora | Stephen Zakrewsky

They then employed a recursive technique to discover new patterns. This site uses cookies. The approach described in this paper is different in that only one sample of a relation needs to frpm found in a text to be useful. For example, the was found where steatornis is a species of bird.

CiteSeerX — Automatic Acquisition of Hyponyms from Large Text Corpora

Good patterns almost always indicate the relation of interest, and they can be recognized with little or no pre-encoded knowledge. If both words were in WordNet but the relation was not, then a new hyponym connection was suggested. Statistical auyomatic have also been used that look to determine lexical relations by looking at very large text samples. Automatically finding hyponyms are useful for assisting in many language tasks.

To find out more, including how to control cookies, see here: You are commenting using your Facebook account. Then repeat, starting at step 2. Text corpus Search for additional papers on this topic.

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