Proximity Window Context Method for Term Extraction in Ontology Learning from Text. Abramowicz, W. & Wisniewski, M. 2008 19th International Conference on Database and Expert Systems Applications, Ieee, 2008.
Proximity Window Context Method for Term Extraction in Ontology Learning from Text [link]Website  abstract   bibtex   
The ontology learning from text cycle consists of the consecutive phases of term, synonym, concept, taxonomy and relation extraction. The paper touches the problems of a low efficiency in the current term extraction methods which are handled by a combination of statistic (frequency-based) and linguistic approaches.We present a novel method to extract terms that uses only shallow linguistic information. It is proposed to explore a different set of linguistic layers and support a classic POS n-gram model with additional context information based on proximity window features. The method is evaluated on two substantially different corpora to produce better results than the classic measures, including standard n-gram models and frequency-based approaches.
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 title = {Proximity Window Context Method for Term Extraction in Ontology Learning from Text},
 type = {article},
 year = {2008},
 identifiers = {[object Object]},
 pages = {215-219},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4624718},
 publisher = {Ieee},
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 created = {2011-12-29T19:53:53.000Z},
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 abstract = {The ontology learning from text cycle consists of the consecutive phases of term, synonym, concept, taxonomy and relation extraction. The paper touches the problems of a low efficiency in the current term extraction methods which are handled by a combination of statistic (frequency-based) and linguistic approaches.We present a novel method to extract terms that uses only shallow linguistic information. It is proposed to explore a different set of linguistic layers and support a classic POS n-gram model with additional context information based on proximity window features. The method is evaluated on two substantially different corpora to produce better results than the classic measures, including standard n-gram models and frequency-based approaches.},
 bibtype = {article},
 author = {Abramowicz, Witold and Wisniewski, Marek},
 journal = {2008 19th International Conference on Database and Expert Systems Applications}
}

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