Evaluating Various Linguistic Features on Semantic Relation Extraction

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Evaluating Various Linguistic Features on Semantic Relation Extraction
Authors
Marcos Garcia
Pablo Gamallo
Publication date
2011
ISSN
13138502
Links

Evaluating Various Linguistic Features on Semantic Relation Extraction - scientific work about Wikipedia quality published in 2011, written by Marcos Garcia and Pablo Gamallo.

Overview

Machine learning approaches for Information Extraction use different types of features to acquire semantically related terms from free text. These features may contain several kinds of linguistic knowledge: from orthographic or lexical to more complex features, like PoStags or syntactic dependencies. In this paper authors select fourmain types of linguistic features and evaluate their performance in a systematic way. Despite the combination of some types of features allows us to improve the f-score of the extraction, authors observed that by adjusting the positive and negative ratio of the training examples, authors can build high quality classifiers with just a single type of linguistic feature, based on generic lexico-syntactic patterns. Experiments were performed on the Portuguese version of Wikipedia.

Embed

Wikipedia Quality

Garcia, Marcos; Gamallo, Pablo. (2011). "[[Evaluating Various Linguistic Features on Semantic Relation Extraction]]". ICIC Express Letters Volume 5, Issue 12, December 2011, pp. 4395-4401. ISSN: 13138502.

English Wikipedia

{{cite journal |last1=Garcia |first1=Marcos |last2=Gamallo |first2=Pablo |title=Evaluating Various Linguistic Features on Semantic Relation Extraction |date=2011 |issn=13138502 |url=https://wikipediaquality.com/wiki/Evaluating_Various_Linguistic_Features_on_Semantic_Relation_Extraction |journal=ICIC Express Letters Volume 5, Issue 12, December 2011, pp. 4395-4401}}

HTML

Garcia, Marcos; Gamallo, Pablo. (2011). &quot;<a href="https://wikipediaquality.com/wiki/Evaluating_Various_Linguistic_Features_on_Semantic_Relation_Extraction">Evaluating Various Linguistic Features on Semantic Relation Extraction</a>&quot;. ICIC Express Letters Volume 5, Issue 12, December 2011, pp. 4395-4401. ISSN: 13138502.