Mining Metalinguistic Activity in Corpora to Create Lexical Resources Using Information Extraction Techniques: the MOP System

Mining metalinguistic activity in corpora to create lexical resources using Information Extraction techniques: the MOP system Carlos Rodríguez Penagos Language Engineering Group, Engineering Institute UNAM, Ciudad Universitaria A.P. 70-472 Coyoacán 04510 Mexico City, México CRodriguezP@iingen.unam.mx Abstract This paper describes and evaluates MOP, an IE system for automatic extraction of metalinguistic information from technical and scientific documents. We claim that such a system can create special databases to boot- strap compilation and facilitate update of the huge and dynamically changing glossaries, knowledge bases and ontologies that are vital to modern-day research. 1 Introduction Availability of large-scale corpora has made it possible to mine specific knowledge from free or semi-structured text, resulting in what many con- sider by now a reasonably mature NLP technolo- gy. Extensive research in Information Extraction (IE) techniques, especially with the series of Mes- sage Understanding Conferences of the nineties, has focused on tasks such as creating and updating databases of corporate join ventures or terrorist and guerrilla attacks, while the ACQUILEX pro- ject used similar methods for creating lexical da- tabases using the highly structured environment of machine-readable dictionary entries and other re- sources. Gathering knowledge from unstructured text often requires manually crafting knowledge- engineering rules both complex and deeply de- pendent of the domain at hand, although some successful experiences using learning algorithms have been reported (Fisher et al., 1995; Chieu et al., 2003). Although mining specific semantic relations and subcategorization information from free-text has been successfully carried out in the past (Hearst, 1999; Manning, 1993), automatically ex- tracting lexical resources (including terminologi- cal definitions) from text in special domains has been a field less explored, but recent experiences (Klavans et al., 2001; Rodríguez, 2001; Cartier, 1998) show that compiling the extensive resources that modern scientific and technical disciplines need in order to manage the explosive growth of their knowledge, is both feasible and practical. A good example of this NLP-based processing need is the MedLine abstract database maintained by the National Library of Medicine1 (NLM), which incorporates around 40,000 Health Sciences pa- pers each month. Researchers depend on these electronic resources to keep abreast of their rapid- ly changing field. In order to maintain and update vital indexing references such as the Unified Me- dical Language System (UMLS) resources, the MeSH and SPECIALIST vocabularies, the NLM staff needs to review 400,000 highly-technical papers each year. Clearly, neology detection, ter- minological information update and other tasks can benefit from applications that automatically search text for information, e.g., when a new term is introduced or an existing one is modified due to data or theory-driven concerns, or, in general, when new information about sublanguage usage is being put forward. But the usefulness of robust NLP applications for special-domain text goes beyond glossary updates. The kind of categoriza- tion information implicit in many definitions can help improve anaphora resolution, semantic ty- ping or acronym identification in these corpora, as well as enhance “semantic rerendering” of spe- cial-domain ontologies and thesaurii (Pustejovsky et al., 2002). In this paper we …

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