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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Lastly, if one or both noun phrases were not in WordNet, then the words and their relation were suggested. When comparing to WordNet, relations were restricted to only nouns without modifiers. Then repeat, starting at step 2.

Automatic Acquisition of Hyponyms from Large Text Corpora

BrentRobert C. This paper looks at extracting information from raw text. You are commenting using your Facebook account. Topics Discussed in This Paper. 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.

Choose a lexical relation that is of interest.

To find out more, including how to control cookies, see here: Noun synsets are organized hierarchically by the hyponymy relation. Gather terms for which this relation holds.

Fill in your details below or click an icon to log in: Semantic Scholar estimates hypponyms this publication has 3, citations based on the available data. WordNet contains 34, noun forms and 26, synsets. By continuing to use this website, hyplnyms agree to their use. This paper has highly influenced other papers. If both noun phrases identified were in WordNet and the hyponym was in the hierarchy, then the result was verified.

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Statistical approaches have also been used that look to corpoda lexical relations by looking at very large text samples. They can be used to augment and verify existing lexicons. Good patterns almost always indicate the relation of interest, and they can be recognized with little or no pre-encoded knowledge. The approach is based on pattern matching.

When comparing against WordNet, three outcomes were considered. This site uses cookies. Automatic acquisition and use of some of the knowledge in physics texts John Batali Other types of relations were tried without success. The researchers found the first pattern manually by looking over texts.

Showing of 21 references. A common issue was underspecification.

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.

Reconciling information contained in separate sentences may be challenging with pattern recognition alone. You are commenting using your Twitter account.

It builds on the success of using pattern recognition for the task of information extraction. They can be used to learn semantics of familiar noun phrases. Citation Statistics 3, Citations 0 ’91 ’97 ’04 ’11 ‘ Patterns The approach is based on pattern matching.

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Sorry, your blog cannot share posts by email. Two goals motivate the approach: Citations Publications citing this paper. You are commenting using your WordPress. CuttingJulian KupiecJan O. The base pattern that the researchers started with wasand they presented the five others shown below. From This Paper Figures, tables, and topics from this paper. Find locations in the text corpus where these expressions occur near each other.

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Good patterns occur frequently and in many text genres. For them, it was different subsets of the hyponym relation.

Automatic Acquisition of Hyponyms from Large Text Corpora | Stephen Zakrewsky

Leave a Reply Cancel reply Enter your comment here This paper has 3, citations. The relation missed the needed information about the kind of species. Shortcomings When comparing to WordNet, relations were restricted to only nouns without modifiers.

Showing of 2, extracted citations. References Publications referenced by this paper.