DefExt: A Semi Supervised Definition Extraction Tool
This tool addresses definition extraction for researchers and practitioners in natural language processing, but it is incremental as it builds on existing methods like CRFs and bootstrapping.
The authors tackled the problem of extracting definitions from text by developing DefExt, a semi-supervised tool that uses Conditional Random Fields and bootstrapping, achieving results validated through automatic and manual evaluations.
We present DefExt, an easy to use semi supervised Definition Extraction Tool. DefExt is designed to extract from a target corpus those textual fragments where a term is explicitly mentioned together with its core features, i.e. its definition. It works on the back of a Conditional Random Fields based sequential labeling algorithm and a bootstrapping approach. Bootstrapping enables the model to gradually become more aware of the idiosyncrasies of the target corpus. In this paper we describe the main components of the toolkit as well as experimental results stemming from both automatic and manual evaluation. We release DefExt as open source along with the necessary files to run it in any Unix machine. We also provide access to training and test data for immediate use.