A. Haghighi

Publications
Influence

Learning Bilingual Lexicons from Monolingual Corpora

A. Haghighi, Percy Liang, Taylor Berg-Kirkpatrick, D. Klein
Computer Science
ACL
1 June 2008

We present a method for learning bilingual translation lexicons from monolingual corpora. Expand

Exploring Content Models for Multi-Document Summarization

A. Haghighi, Lucy Vanderwende
Computer Science
HLT-NAACL
31 May 2009

We present an exploration of generative probabilistic models for multi-document summarization. Expand

Prototype-Driven Learning for Sequence Models

A. Haghighi, D. Klein
Computer Science
HLT-NAACL
4 June 2006

We investigate prototype-driven learning for primarily unsupervised sequence modeling. Prior knowledge is specified declaratively, by providing a few canonical examples of each target annotation… Expand

Joint Learning Improves Semantic Role Labeling

Kristina Toutanova, A. Haghighi, Christopher D. Manning
Computer Science
ACL
25 June 2005

We propose a discriminative log-linear joint model for semantic role labeling, which incorporates more global features and achieves superior performance in comparison to state-of-the art models. Expand

Structured Relation Discovery using Generative Models

Limin Yao, A. Haghighi, S. Riedel, A. McCallum
Computer Science
EMNLP
27 July 2011

We propose a series of generative probabilistic models, broadly similar to topic models, each which generates a corpus of observed triples of entity mention pairs and the surface syntactic dependency path between them. Expand

Simple Coreference Resolution with Rich Syntactic and Semantic Features

A. Haghighi, D. Klein
Computer Science
EMNLP
6 August 2009

We present a simple approach which completely modularizes these three aspects. Expand

A Global Joint Model for Semantic Role Labeling

Kristina Toutanova, A. Haghighi, Christopher D. Manning
Computer Science
Computational Linguistics
1 June 2008

We present a model for semantic role labeling that effectively captures the linguistic intuition that a semantic argument frame is a joint structure, with strong dependencies among the arguments. Expand

Unsupervised Coreference Resolution in a Nonparametric Bayesian Model

A. Haghighi, D. Klein
Computer Science
ACL
1 June 2007

We present an unsupervised, nonparametric Bayesian approach to coreference resolution which models both global entity identity across a corpus as well as the sequential anaphoric structure within each document. Expand

Event Discovery in Social Media Feeds

E. Benson, A. Haghighi, R. Barzilay
Computer Science
ACL
19 June 2011

We present a novel method for record extraction from social streams such as Twitter. Expand

Coreference Resolution in a Modular, Entity-Centered Model

A. Haghighi, D. Klein
Computer Science
HLT-NAACL
2 June 2010

We present a generative, model-based approach to coreference resolution in which each of these factors is modularly encapsulated and learned in a primarily unsu-pervised manner, resulting in the best results to date on the complete end-to-end coreference task. Expand

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