A long-standing challenge in coreference resolution has been the incorporation of entity-
level information-features defined over clusters of mentions instead of mention pairs. We …

We propose a new deterministic approach to coreference resolution that combines the
global information and precise features of modern machine-learning models with the …

Abstract Information extraction (IE) is the task of automatically extracting structured
information from unstructured/semi-structured machine-readable documents. Among various …

Classical coreference systems encode various syntactic, discourse, and semantic
phenomena explicitly, using heterogenous features computed from hand-crafted heuristics …

We investigate different ways of learning structured perceptron models for coreference
resolution when using non-local features and beam search. Our experimental results …

Coreference resolution is a well known clustering task in Natural Language Processing. In
this paper, we describe the Latent Left Linking model (L3M), a novel, principled, and …

Coreference resolution metrics quantify errors but do not analyze them. Here, we consider
an automated method of categorizing errors in the output of a coreference system into …

We describe a structure learning system for unrestricted coreference resolution that explores
two key modeling techniques: latent coreference trees and automatic entropy-guided feature …

The general method of machine learning has encountered disadvantages in terms of the
significant amount of time and effort required for feature extraction and engineering in …

We describe a scaffolding approach to the task of coreference resolution that incrementally
combines statistical classifiers, each designed for a particular mention type, with rule-based …