A Linear Programming Formulation for Global Inference in Natural Language Tasks

Given a collection of discrete random variables representing outcomes of learned local predictors in natural language, e.g., named entities and relations, we seek an optimal global assignment to the variables in the presence of general (non-sequential) constraints. Examples of these constraints include the type of arguments a relation can take, and the mutual activity of different relations, etc. We develop a linear programming formulation for this problem and evaluate it in the context of simultaneously learning named entities and relations. Our approach allows us to efficiently incorporate domain and task specific constraints at decision time, resulting in significant improvements in the accuracy and the ``human-like'' quality of the inferences.

RothYi-CoNLL04.pdf
PDF file

In  Proceedings of the 8th Conference on Computational Natural Language Learning (CoNLL-04)

Publisher  Association for Computational Linguistics
All copyrights reserved by ACL 2004

Details

TypeInproceedings
Pages1–8
> Publications > A Linear Programming Formulation for Global Inference in Natural Language Tasks