This paper focuses on the study of recognizing discontiguous entities. Motivated by a previous work, we propose to use a novel hypergraph representation to jointly encode discontiguous entities of unbounded length, which can overlap with one another. To compare with existing approaches, we first formally introduce the notion of model ambiguity, which defines the difficulty level of interpreting the outputs of a model, and then formally analyze the theoretical advantages of our model over previous existing approaches based on linearchain CRFs. Our empirical results also show that our model is able to achieve significantly better results when evaluated on standard data with many discontiguous entities.
Recommended citation: Muis, A. O., & Lu, W. (2016). Learning to Recognize Discontiguous Entities. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 75–84). Stroudsburg, PA, USA: Association for Computational Linguistics. https://doi.org/10.18653/v1/D16-1008