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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Showing of 2, extracted citations. Patterns The approach is based on pattern matching. When comparing against WordNet, three outcomes were considered. Then repeat, starting at step 2.

BrentRobert C. The approach is based on pattern matching.

Automatic Acquisition of Hyponyms from Large Text Corpora

Noun synsets are organized hierarchically by the hyponymy relation. If both noun phrases identified were in WordNet and the hyponym was in the hierarchy, then the result was verified. Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in: Statistical approaches have also been used that look to determine lexical relations by looking at very large text samples.

Grolier Electronic Publishing, Danbury…. This lqrge has 3, citations. If both words were in WordNet but the relation was not, then a new hyponym connection was suggested. CuttingJulian AufomaticJan O.

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Automatic Acquisition of Hyponyms from Large Text Corpora – ACL Anthology

Showing of 21 references. To find out more, including how to control cookies, see here: The approach described in this paper is acquisitioon in that only one sample of a relation needs to be found in a text to be useful.

When comparing to WordNet, relations were restricted frim only nouns without modifiers. Choose a lexical relation that is of interest. Skip to search form Skip to main content.

Reconciling information contained in separate sentences may be challenging with pattern recognition alone. Similarly, the relation can be understood by relaxing the ISA definition of hyponym to one of close semantic similarity.

References Publications referenced by this paper.

CiteSeerX — Automatic Acquisition of Hyponyms from Large Text Corpora

Email required Address never made public. The paper presents ftom method for automatic acquisition of hyponymy relations from raw text. For example, the was found where steatornis is a species of bird.

Notify me of new comments via email. They can be used to augment and verify existing lexicons. Text corpus Search for additional automahic on this topic.

The researchers found the first pattern manually by looking over texts.

This information may have been contained in a previous sentence. By continuing to use this website, you agree to their use. Good patterns occur frequently and in many text genres. Find locations in the text corpus where these expressions occur near each other. Gather terms for which this relation holds.

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Automatic acquisition and use of some of the knowledge in physics texts John Batali 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. Appositives were difficult to match accurately.

For them, it was different subsets of the hyponym relation. Topics Discussed in This Paper.

A common issue was underspecification. Other types of relations were tried without success. They then employed a recursive technique to discover new patterns. Good patterns almost always indicate the relation of interest, and they can be recognized with little or no pre-encoded knowledge. You are commenting using your WordPress. Shortcomings When comparing to WordNet, relations were restricted to only nouns without modifiers.