Paper presented at International Conference on Recent Advances in Natural Language Processing 2015 (RANLP 2015)

Definition Extraction (DE) is the task to extract textual definitions from naturally occurring text. It is gaining popularity as a prior step for constructing taxonomies, ontologies, automatic glossaries or dictionary entries. These fields of application motivate greater interest in well-formed encyclopedic text from which to extract definitions, and therefore DE for academic or lay discourse has received less attention. In this paper we propose a weakly supervised bootstrapping approach for identifying textual definitions with higher linguistic variability than the classic encyclopedic genus-et-differentia definition, and take the domain of Natural Language Processing as a use case. We also introduce a novel set of features for DE and explore their relevance. Evaluation is carried out on two datasets that reflect opposed ways of expressing definitional knowledge.

This work is partially funded by the SKATER project, TIN2012-38584-C06-03, Ministerio de Econom´ıa y Competitividad, Secretar´ıa de Estado de Investigaci´on, Desarrollo e Innovaci´on, Espa˜na; and Dr. Inventor (FP7-ICT-2013.8.1 611383).