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We describe a structure learning system for unrestricted coreference resolution that explores two key modeling techniques: latent coreference trees and automatic entropy-guided feature induction. The ...
This work is focused on research in machine learning for coreference resolution. Coreference resolution is a natural language processing task that consists of determining the expressions in a discours...
We propose a new deterministic approach to coreference resolution that combines the global information and precise features of modern machine-learning models with the transparency and modularity of de...
In this paper, we present a learning approach to coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not ju...
A desirable quality of a coreference resolution system is the ability to handle transitivity constraints, such that even if it places high likelihood on a particular mention being coreferent with each...
This paper details the coreference resolution system submitted by Stanford at the CoNLL- 2011 shared task. Our system is a collection of deterministic coreference resolution models that incorporate le...
Most coreference resolution models determine if two mentions are coreferent using a single function over a set of constraints or features.This approach can lead to incorrect decisions as lower precisi...
We introduce a novel coreference resolution system that models entities and events jointly. Our iterative method cautiously constructs clusters of entity and event mentions using linear regression to ...
We propose a new deterministic approach to coreference resolution that combines the global information and precise features of modern machine-learning models with the transparency and modularity of de...
Gesture Salience as a Hidden Variable for Coreference Resolution and Keyframe Extraction

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