Coreference resolution, determining the appropriate discourse referent for an anaphoric expression, is an essential but difficult task in natural language processing. It has been observed that an important source of errors in machine-learning based approaches to this task, is the wrong disambiguation of the third person singular neuter pronoun as either referential or non-referential. In this p...

Research on coreference resolution and summarization has modeled the way entities are realized as concrete phrases in discourse. In particular there exist models of the noun phrase syntax used for discourse-new versus discourse-old referents, and models describing the likely distance between a pronoun and its antecedent. However, models of discourse coherence, as applied to information ordering...

In this paper we present our winning system in the WMT16 Shared Task on CrossLingual Pronoun Prediction, where the objective is to predict a missing target language pronoun based on the target and source sentences. Our system is a deep recurrent neural network, which reads both the source language and target language context with a softmax layer making the final prediction. Our system achieves ...

Much effort in the research community has been spent on solving the anaphora resolution or pronoun resolution problem, and in particular for news texts. In order to selectively inherit the previous works and solve the same problem for a new domain, we carried out a comparative study with three different corpora: MUC, ACE for the news texts, and GENIA for bio-medical papers. Our corpus analysis ...

We propose a method to alleviate the problem of referential granularity for Japanese zero pronoun resolution. We use dictionary definition sentences to extract ‘representative’ arguments of predicative definition words; e.g. ‘arrest’ is likely to take police as the subject and criminal as its object. These representative arguments are far more informative than ‘person’ that is provided by other...