Automatic taxonomy extraction in different languages using wikipedia and minimal language-specific information
|Automatic taxonomy extraction in different languages using wikipedia and minimal language-specific information|
|Author(s)||Dominguez Garcia R., Schmidt S., Rensing C., Steinmetz R.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Hyponymy Detection, Multilingual large-scale taxonomies, NLP, Wikipedia Mining (Extra: Category systems, Hyponymy, Hyponymy relation, Knowledge base, Knowledge basis, Multilingual approach, Multiple languages, NLP, Semantic web applications, Wikipedia, Wordnet, Computational linguistics, Knowledge based systems, Natural language processing systems, Taxonomies, Text processing, Websites)|
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Automatic taxonomy extraction in different languages using wikipedia and minimal language-specific information is a 2012 conference paper written in English by Dominguez Garcia R., Schmidt S., Rensing C., Steinmetz R. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Knowledge bases extracted from Wikipedia are particularly useful for various NLP and Semantic Web applications due to their co- verage, actuality and multilingualism. This has led to many approaches for automatic knowledge base extraction from Wikipedia. Most of these approaches rely on the English Wikipedia as it is the largest Wikipedia version. However, each Wikipedia version contains socio-cultural knowledge, i.e. knowledge with relevance for a specific culture or language. In this work, we describe a method for extracting a large set of hyponymy relations from the Wikipedia category system that can be used to acquire taxonomies in multiple languages. More specifically, we describe a set of 20 features that can be used for for Hyponymy Detection without using additional language-specific corpora. Finally, we evaluate our approach on Wikipedia in five different languages and compare the results with the WordNet taxonomy and a multilingual approach based on interwiki links of the Wikipedia.
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