NDLI: Building Ontologies from Text Corpora

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Building Ontologies from Text Corpora

Content Provider	ACM Digital Library
Author	Mazouzi, Smaine Benafia, Sara Benafia, Ali
Abstract	This paper presents a novel approach of information extraction for building ontologies covering an extensive range of applications drawn from corpora. Our goal is to propose a method that is independent of domains and based on a distributional analysis of semantic units to bring out all the candidates informative elements (concepts, entities, semantic relations, named entities .). This method is based on a pipeline of four main stages allowing to refine the extraction information from unstructured text in the form of a suite of decomposable representations (sentences in triplets, "argumental structure".) until to get a consistent final ontology. We applied the pipeline defined in the context of a repeated sampling of 100 articles randomly drawn from text corpus (`Le Monde' with annual version `2013'). For the evaluation results of the trial implementation of our system, we have achieved a level of accuracy at which was up to 74%. We believe from the results obtained that our methodology is quite generic, and can be easily adapted to any new domain.
Starting Page	1
Ending Page	20
Page Count	20
File Format	PDF
ISBN	9781450334181
DOI	10.1145/2832987.2833029
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text analysis Ontology Similarity measure Linguistic processing Terminology recognition Information extraction
Content Type	Text
Resource Type	Article

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