Coreference resolution aims at resolving repeated references to an object in a document
and forms a core component of natural language processing (NLP) research. When used as …

The past decade has seen a tremendous growth in the amount of experimental and
computational biomedical data, specifically in the areas of genomics and proteomics. This …

Text mining is a new and exciting area of computer science research that tries to solve the
crisis of information overload by combining techniques from data mining, machine learning …

This paper presents a framework for localization or grounding of phrases in images using a
large collection of linguistic and visual cues. We model the appearance, size, and position of …

Teaching computers to solve language problems is one of the major challengesof natural
language processing. There is a large amount of interesting researchdevoted to this field …

Syntactic simplification is the process of reducing the grammatical complexity of a text, while
retaining its information content and meaning. The aim of syntactic simplification is to make …

Traditional learning-based coreference resolvers operate by training a mentionpair classifier
for determining whether two mentions are coreferent or not. Two independent lines of recent …

Centering theory is the best-known framework for theorizing about local coherence and
salience; however, its claims are articulated in terms of notions which are only partially …

Anaphora resolution is a complicated problem in Natural Language Processing and has
attracted the attention of many researchers. The approaches developed-traditional (from …

This paper proposes a new approach for coreference resolution which uses the Bell tree to
represent the search space and casts the coreference resolution problem as finding the best …