An integrated, conditional model of information extraction and coreference with application to citation matching
ScholarWorks@UMassAmherst (University of Massachusetts Amherst)Published 7 July 2004Open access
Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay
Citations126
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
Although information extraction and coreference resolution appear together in many applications, most current systems perform them as independent steps. This paper describes an approach to integrated inference for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advantages of conditional probability training, and of a coreference model structure based on graph partitioning. On a data set of research paper citations, we show significant reduction in error by using extraction uncertainty to improve coreference citation matching accuracy, and using coreference to improve the accuracy of the extracted fields. 1
Keywords
Computer ScienceDecision Sciences
ScholarlyCommons (University of Pennsylvania)Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
12,978 Citations2001John Lafferty, Andrew McCallum +1 more
This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
IEEE Transactions on Pattern Analysis and Machine IntelligenceFast approximate energy minimization via graph cuts
6,987 Citations2001Yuri Boykov, Olga Veksler +1 more
Journal of the Royal Statistical Society Series B (Statistical Methodology)On the Statistical Analysis of Dirty Pictures
4,228 Citations1986Julian Besag
A simple, iterative method of reconstruction is proposed, which does not depend on these large-scale characteristics of the random field, and is illustrated by computer simulations in which the original scene is not directly related to the assumed random field.
Shallow parsing with conditional random fields
1,251 Citations2003Fei Sha, Fernando Pereira
This work shows how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model.
Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons
1,161 Citations2003Andrew McCallum, Wei Li
This work has shown that conditionally-trained models, such as conditional maximum entropy models, handle inter-dependent features of greedy sequence modeling in NLP well.
Generalized Belief Propagation
906 Citations2000Jonathan S. Yedidia, William T. Freeman +1 more
It is shown that BP can only converge to a stationary point of an approximate free energy, known as the Bethe free energy in statistical physics, and generalized belief propagation (GBP) versions of these Kikuchi approximations are derived.
Dynamic conditional random fields
746 Citations2004Charles Sutton, Khashayar Rohanimanesh +1 more
Projective image restoration using sparsity regularization
735 Citations2013Nantheera Anantrasirichai, Jeremy F. Burn +1 more
This paper presents a method of image restoration for projective ground images which lie on a projection orthogonal to the camera axis and proposes instant estimation of a blur kernel arising from the projective transform and the subsequent interpolation of sparse data.
International Conference on Acoustics, Speech, and Signal ProcessingSome statistical issues in the comparison of speech recognition algorithms
643 Citations2003Larry Gillick, Simón Cox
The authors present two simple tests for deciding whether the difference in error rates between two algorithms tested on the same data set is statistically significant.
Discriminative probabilistic models for relational data
637 Citations2002Ben Taskar, Pieter Abbeel +1 more
ComputerDigital libraries and autonomous citation indexing
614 Citations1999Sandra Lawrence, C. Lee Giles +1 more
Digital libraries incorporating ACI can help organize scientific literature and may significantly improve the efficiency of dissemination and feedback and speed the transition to scholarly electronic publishing.
A Comparison of String Metrics for Matching Names and Records
481 Citations2003W. W. Cohen and P. Ravikumar and S. Fienberg
An open-source Java toolkit of methods for matching names and records is described and results obtained from using various string distance metrics on the task of matching entity names are summarized.
Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)A Linear Programming Formulation for Global Inference in Natural Language Tasks
442 Citations2004Dan Roth, Wen-tau Yih
This work develops a linear programing formulation for this problem and evaluates it in the context of simultaneously learning named entities and relations to efficiently incorporate domain and task specific constraints at decision time, resulting in significant improvements in the accuracy and the "human-like" quality of the inferences.
arXiv (Cornell University)Efficiently Inducing Features of Conditional Random Fields
366 Citations2012Andrew McCallum
Table extraction using conditional random fields
355 Citations2003David Pinto, Andrew McCallum +2 more
Unlike HMMs, CRFs support the use of many rich and overlapping layout and language features, and as a result, they perform significantly better, and are compared with hidden Markov models (HMMs).
Identity Uncertainty and Citation Matching
275 Citations2002Hanna Pasula, Bhaskara Marthi +3 more
This approach is based on the use of a relational probability model to define a generative model for the domain, including models of author and title corruption and a probabilistic citation grammar, and shows that the method outperforms current algorithms for citation matching.
ScholarWorks@UMassAmherst (University of Massachusetts Amherst)Accurate Information Extraction from Research Papers using Conditional Random Fields
272 Citations2004Fuchun Peng, Andrew McCallum
New state-of-the-art performance is achieved on a standard benchmark data set, reducing error in average F1 by 36%, and word error rate by 78% in comparison with the previous best SVM results.
Exploiting Tractable Substructures in Intractable Networks
221 Citations1995Lawrence K. Saul, Michael I. Jordan
A refined mean field approximation for inference and learning in probabilistic neural networks is developed, and it is shown how to incorporate weak higher order interactions into a first-order hidden Markov model.
Named Entity Extraction using AdaBoost
185 Citations2002Xavier Carreras, Lluı́s Màrquez +1 more
This paper presents a Named Entity Extraction (NEE) system for the CoNLL 2002 competition, which makes use of binary AdaBoost classifiers.
ScholarWorks@UMassAmherst (University of Massachusetts Amherst)Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference
150 Citations2003Andrew McCallum, Ben Wellner
This paper introduces several discriminative, conditionalprobability models for coreference analysis, all examples of undirected graphical models that can incorporate a great variety of features of the input without having to be concerned about their dependencies.
Lecture notes in computer scienceCorrelation Clustering with Partial Information
116 Citations2003Erik D. Demaine, Nicole Immorlica
A Mutually Beneficial Integration of Data Mining and Information Extraction
86 Citations2000Un Yong Nahm, Raymond J. Mooney
A system called DISCOTEX is described, that combines IE and data mining methodologies to perform text mining as well as improve the performance of the underlying extraction system.
arXiv (Cornell University)Variational Approximations between Mean Field Theory and the Junction\n Tree Algorithm
80 Citations2013Wim Wiegerinck
ScholarWorks@UMassAmherst (University of Massachusetts Amherst)A Note on the Unification of Information Extraction and Data Mining using Conditional-Probability, Relational Models
56 Citations2003Andrew McCallum, David Jensen
This position paper proposes the use of unified, relational, undirected graphical models for information extraction and data mining, in which extraction decisions and data-mining decisions are made in the same probabilistic “currency” with a common inference procedure— each component thus being able to make up for the weaknesses of the other and therefore improving the performance of both.
Autocorrelation and Relational Learning: Challenges and Opportunities
25 Citations2004Jennifer Neville, Özgür Şimşek +1 more
An overview of research on autocorrelation in a number of fields with an emphasis on implications for relational learning is provided, and a numberof challenges and opportunities for model learning and inference are outlined.
First-Order Probabilistic Models for Information Extraction
23 Citations2003Bhaskara Marthi, Brian Milch +1 more
It is argued that first-order probabilistic models (FOPMs) are a promising framework for IE, for two main reasons, and how the desired inference patterns emerge from the model is explained.
Proceedings - International Conference on Image ProcessingAnalysis of an iterative dynamic programming approach to 2-D phase unwrapping
2 Citations2003Ying Lei, Brendan J. Frey +2 more
An approximate upper bound for the probability of pixel error based on a Gaussian Markov random field model is derived and a comparison with other phase unwrapping techniques on a real data set suggests that the new approach is superior.