Narrowing the Modeling Gap: A Cluster-Ranking Approach to Coreference Resolution
Traditional learning-based coreference resolvers operate by training the mention-pair model for determining whether two mentions are coreferent or not. Though conceptually simple and easy to understand, the mention-pair model is linguistically rather unappealing and lags far behind the heuristic-based coreference models proposed in the pre-statistical NLP era in terms of sophistication. Two independent lines of recent research have attempted to improve the mention-pair model, one by acquiring the mention-ranking model to rank preceding mentions for a given anaphor, and the other by training the entity-mention model to determine whether a preceding cluster is coreferent with a given mention. We propose a cluster-ranking approach to coreference resolution, which combines the strengths of the mention-ranking model and the entity-mention model, and is therefore theoretically more appealing than both of these models. In addition, we seek to improve cluster rankers via two extensions: (1) lexicalization and (2) incorporating knowledge of anaphoricity by jointly modeling anaphoricity determination and coreference resolution. Experimental results on the ACE data sets demonstrate the superior performance of cluster rankers to competing approaches as well as the effectiveness of our two extensions.
Code (0)
등록된 구현이 없습니다.
Tasks
coreference-resolutionCoreference ResolutionSimilar Papers 제목 키워드 기반
Latent Structures for Coreference Resolution
Machine learning approaches to coreference resolution vary greatly in the modeling of the problem: while early approaches operated on the mention pair level, current research focuses on ranking architectures and antecede…
BIG-bench Machine Learningcoreference-resolutionCoreference ResolutionMachine Translation+1Higher-order Coreference Resolution with Coarse-to-fine Inference
We introduce a fully differentiable approximation to higher-order inference for coreference resolution. Our approach uses the antecedent distribution from a span-ranking architecture as an attention mechanism to iterativ…
Coreference ResolutionA Cluster Ranking Model for Full Anaphora Resolution
Anaphora resolution (coreference) systems designed for the CONLL 2012 dataset typically cannot handle key aspects of the full anaphora resolution task such as the identification of singletons and of certain types of non-…
Coreference ResolutionUnsupervised Ranking Model for Entity Coreference Resolution
Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community. In this paper, we propose a generative, unsupervi…
coreference-resolutionCoreference ResolutionmodelOkay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation
In NLP, Event Coreference Resolution (ECR) is the task of connecting event clusters that refer to the same underlying real-life event, usually via neural systems. In this work, we investigate using abductive free-text ra…
Clusteringcoreference-resolutionCoreference ResolutionEvent Coreference Resolution+1