Papers for Relevance Assessment by Heng Ji

Research Question: We present a novel mechanism for improving reference resolution by using the output of a relation tagger to rescore coreference hypotheses.

Reformulation: improving reference resolution by using the output of a relation tagger to rescore coreference hypotheses.

Paper ID
(Link to PDF)

Title

Author(s)

4-zelenko

Coreference Resolution for Information Extraction

Dmitry Zelenko, Chinatsu Aone and Jason Tibbetts

W99-0207

Corpus-Based Anaphora Resolution Towards Antecedent Preference

Michael PAUL; Kazuhide YAMAMOTO; Eiichiro SUMITA

W97-1308

Supporting anaphor resolution in dialogues with a corpus-based probabilistic model

Marco ROCHA

5-heng

Applying Coreference to Improve Name Recognition

Heng Ji and Ralph Grishman

P03-1023

Coreference Resolution Using Competition Learning Approach

Xiaofeng Yang; Guodong Zhou; Jian Su; Chew Lim Tan

X98-1021

Overview of the University of Pennsylvania's TIPSTER Project

Breck Baldwin; Thomas S. Morton; Amit Bagga

W03-1903

Ontology-based Linguistic Annotation

Philipp Cimiano; Siegfried Handschuh

A00-1020

Multilingual Coreference Resolution

Sanda M. Harabagiu; Steven J. Maiorano

74-958

Optimizing Algorithms for Pronoun Resolution

Michael Schiehlen

W97-0319

Probabilistic Coreference in Information Extraction

Andrew Kehler

H91-1013

Integration of Diverse Recognition Methodologies Through Reevaluation of N-Best Sentence Hypotheses

M. Ostendorf; A. Kannan; S. Austin; O. Kimball; R. Schwartz; J.R. Rohlicek

P05-1051

Improving Name Tagging by Reference Resolution and Relation Detection

Heng Ji; Ralph Grishman

N01-1008

Text and Knowledge Mining for Coreference Resolution

Sanda M. Harabagiu; Razvan C. Bunescu; Steven J. Maiorano

W99-0634

Corpus-Based Learning for Noun Phrase Coreference Resolution

Wee Meng Soon; Hwee Tou Ng; Chung Yong Lim

P03-1022

A Machine Learning Approach to Pronoun Resolution in Spoken Dialogue

Michael Strube; Christoph Muller