Authors:
Ahmad Issa Alaa Aldine
1
;
Mounira Harzallah
2
;
Berio Giuseppe
3
;
Nicolas Béchet
3
and
Ahmad Faour
4
Affiliations:
1
IRISA, University Bretagne Sud, France, Lebanese University and Lebanon
;
2
LINA, University of Nantes and France
;
3
IRISA, University Bretagne Sud and France
;
4
Lebanese University and Lebanon
Keyword(s):
Ontology Learning, Hypernym Extraction, Dependency Parser, Hearst Patterns.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Knowledge Acquisition
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Symbolic Systems
Abstract:
Hypernym relation extraction is considered the backbone of building ontologies. Hearst patterns are the most popular patterns used to extract hypernym relation. They include POS tags and lexical information, and they are applied on a shallow parsed corpora. In this paper, we propose a new formalization of Hearst patterns using dependency parser, called Dependency Hearst patterns. This formalization allows them to match better complex or ambiguous sentences. To evaluate our proposal, we have compared the performance of Dependency Hearst patterns to that of the lexico-syntactic Hearst patterns, applied on a music corpus. Dependency Hearst patterns yield a better result than lexico-syntactic patterns for extracting hypernym relations from the corpus.