Paper: A Discriminative Alignment Model for Abbreviation Recognition

ACL ID C08-1083
Title A Discriminative Alignment Model for Abbreviation Recognition
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

This paper presents a discriminative align- ment model for extracting abbreviations and their full forms appearing in actual text. The task of abbreviation recognition is formalized as a sequential alignment problem, which finds the optimal align- ment (origins of abbreviation letters) be- tween two strings (abbreviation and full form). We design a large amount of fine- grained features that directly express the events where letters produce or do not pro- duce abbreviations. We obtain the optimal combination of features on an aligned ab- breviation corpus by using the maximum entropy framework. The experimental re- sults show the usefulness of the alignment model and corpus for improving abbrevia- tion recognition.