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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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 does not require parsing nor context specific, preencoded knowledge.
One reason was due the type of tdxt contained in WordNet, but it also was suggested in general that it is difficult to know which modifiers are important to the relation.
Good patterns almost always indicate the relation of interest, and they can be recognized with little or no pre-encoded knowledge. Email required Address never made public. This paper has highly influenced other papers. Patterns The approach is based on pattern matching.
It builds on the success of using pattern recognition for the task of information extraction. Find the commonalities among the locations and hypothesize patterns that indicate the relation of interest.
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. Notify me of new comments via email.
Automatic Acquisition of Hyponyms from Large Text Corpora | Stephen Zakrewsky
Post was not sent – check your email addresses! Grolier Electronic Publishing, Danbury…. You are commenting using your WordPress. Find locations in the text corpus where these expressions occur near each other.
Good patterns occur frequently and in many text genres.
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. Skip to search form Skip to main content. They can be used to learn semantics of familiar noun phrases. When comparing against WordNet, three outcomes were considered. This paper has 3, citations. You are commenting lrage your Facebook account. Noun synsets are organized hierarchically by the hyponymy hypomyms.
Leave a Reply Cancel reply Enter your comment here Sorry, your blog cannot share posts by email. Citations Publications citing this paper. Then repeat, starting at step 2. References Publications referenced by this paper. Citation Statistics 3, Citations 0 ’91 ’97 ’04 ’11 ‘ Automatic acquisition and use of some of the knowledge in physics texts John Batali Once a new pattern is discovered, use it larbe find more instances lage the relation.
The approach is based on pattern matching. A common issue was underspecification. They can be used to augment and verify existing lexicons. When comparing txet WordNet, relations were restricted to only nouns without modifiers. Semantic Scholar estimates that this publication has 3, citations based on the available data.
Automatic Acquisition of Hyponyms from Large Text Corpora
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.
BrentRobert C. See our FAQ for additional information. Topics Discussed in This Paper. For example, the was found where steatornis is a species of bird. To find out more, including how to control cookies, see here: They then employed a recursive technique to discover new patterns. Statistical approaches have also been used that look to determine lexical relations by looking at very large text samples.
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Automatic Acquisition of Hyponyms from Large Text Corpora – Semantic Scholar
Acuisition were difficult to match accurately. If both words were in WordNet but the relation was not, then a new hyponym connection was suggested. Text corpus Search for additional papers on this topic. Gather terms for which this relation holds. Similarly, the relation can be understood by relaxing the ISA definition of hyponym to one of close semantic similarity.
Automatically finding hyponyms are useful for assisting in many language tasks. Two goals motivate the approach: