Tailoring the automated construction of large-scale taxonomies using the web

• Published in: Language Resources and Evaluation Vol. 47; no. 3; pp. 859 - 890
• Main Authors: Kozareva, Zornitsa, Hovy, Eduard
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01.09.2013; Springer Netherlands; Springer Nature B.V
• Subjects: Algorithms; Animal genetics; Automated; Automatic text analysis; Automation; Biological taxonomies; Computational Linguistics; Computer Science; Construction; Dictionaries; Domain ontologies; English language; Hierarchical relationships; Hierarchies; Humans; International conferences; Jargon; Language; Language and Literature; Languages; Linguistics; Mammals; Natural language processing; Ontologies; Ontology; Original Paper; Recognition; Semantic analysis; Semantics; Social Sciences; Taxonomy; Terminology; Texts; Thesauri; United States > US; Text mining; Ontology induction; Hyponym and hypernym learning; Wordnet evaluation
• ISSN: 1574-020X; 1572-8412; 1574-0218
• DOI: 10.1007/s10579-013-9229-0

It has long been a dream to have available a single, centralized, semantic thesaurus or terminology taxonomy to support research in a variety of fields. Much human and computational effort has gone into constructing such resources, including the original WordNet and subsequent wordnets in various languages. To produce such resources one has to overcome well-known problems in achieving both wide coverage and internal consistency within a single wordnet and across many wordnets. In particular, one has to ensure that alternative valid taxonomizations covering the same basic terms are recognized and treated appropriately. In this paper we describe a pipeline of new, powerful, minimally supervised, automated algorithms that can be used to construct terminology taxonomies and wordnets, in various languages, by harvesting large amounts of online domain-specific or general text. We illustrate the effectiveness of the algorithms both to build localized, domain-specific wordnets and to highlight and investigate certain deeper ontological problems such as parallel generalization hierarchies. We show shortcomings and gaps in the manually-constructed English WordNet in various domains.