Towards definition extraction using conditional random fields

Abstract

Paper presented at International Conference Recent Advances in Natural Language Processing RANLP 2013;2013 Sept 9-11; Hissar, Bulgaria.

Definition Extraction (DE) and terminology are contributing to help structuring the overwhelming amount of information available. This article presents KESSI (Knowledge Extraction System for Scientific/nInterviews), a multilingual domainindependent machine-learning approach to the extraction of definitional knowledge, specifically oriented to scientific interviews. The DE task was approached as both a classification and a sequential labelling task. In the latter, figures of Precision, Recall and F-Measure were similar to human annotation, and suggest that combining structural, statistical and linguistic/nfeatures with Conditional Random Fields can contribute significantly to the development of DE systems.