Learning to Recognize Discontiguous Entities

TL;DR

This paper introduces a hypergraph-based model for recognizing discontiguous entities, demonstrating improved accuracy over previous methods by effectively encoding complex entity structures.

Contribution

The paper proposes a novel hypergraph representation for discontiguous entity recognition and introduces the concept of model ambiguity for theoretical analysis.

Findings

Achieves significantly better results on standard datasets

Effectively encodes overlapping and unbounded discontiguous entities

Provides theoretical advantages over linear-chain CRFs

Abstract

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 linear-chain 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.