Collective information extraction with relational Markov networks

Bunescu, Răzvan; Mooney, Raymond J.

doi:10.3115/1218955.1219011

Cited by 82 publications

(70 citation statements)

References 10 publications

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“…Methodologically, our work is similar to collective information extraction with undirected graphical models as proposed by Bunescu et al [4] or Kluegl et al [9]; however, these approaches are limited to problems of text segmentation, entity tagging and extraction of individual relations.…”

Section: Related Workmentioning

confidence: 99%

Assessing the Impact of Single and Pairwise Slot Constraints in a Factor Graph Model for Template-Based Information Extraction

Horst

Hartung

Klinger

et al. 2018

Natural Language Processing and Information Systems

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Abstract. Template-based information extraction generalizes over standard token-level binary relation extraction in the sense that it attempts to fill a complex template comprising multiple slots on the basis of information given in a text. In the approach presented in this paper, templates and possible fillers are defined by a given ontology. The information extraction task consists in filling these slots within a template with previously recognized entities or literal values. We cast the task as a structure prediction problem and propose a joint probabilistic model based on factor graphs to account for the interdependence in slot assignments. Inference is implemented as a heuristic building on Markov chain Monte Carlo sampling. As our main contribution, we investigate the impact of soft constraints modeled as single slot factors which measure preferences of individual slots for ranges of fillers, as well as pairwise slot factors modeling the compatibility between fillers of two slots. Instead of relying on expert knowledge to acquire such soft constraints, in our approach they are directly captured in the model and learned from training data. We show that both types of factors are effective in improving information extraction on a real-world data set of full-text papers from the biomedical domain. Pairwise factors are shown to particularly improve the performance of our extraction model by up to +0.43 points in precision, leading to an F1 score of 0.90 for individual templates.

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Section: Related Workmentioning

confidence: 99%

Assessing the Impact of Single and Pairwise Slot Constraints in a Factor Graph Model for Template-Based Information Extraction

Horst

Hartung

Klinger

et al. 2018

Natural Language Processing and Information Systems

View full text Add to dashboard Cite

show abstract

“…It is aimed at extracting object information from two-dimensionally laid-out web pages. See also [3].…”

Section: Information Extraction Methodsmentioning

confidence: 99%

Tree-Structured Conditional Random Fields for Semantic Annotation

Tang

Hong

et al. 2006

Lecture Notes in Computer Science

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Abstract. The large volume of web content needs to be annotated by ontologies (called Semantic Annotation), and our empirical study shows that strong dependencies exist across different types of information (it means that identification of one kind of information can be used for identifying the other kind of information). Conditional Random Fields (CRFs) are the state-of-the-art approaches for modeling the dependencies to do better annotation. However, as information on a Web page is not necessarily linearly laid-out, the previous linear-chain CRFs have their limitations in semantic annotation. This paper is concerned with semantic annotation on hierarchically dependent data (hierarchical semantic annotation). We propose a Tree-structured Conditional Random Field (TCRF) model to better incorporate dependencies across the hierarchically laid-out information. Methods for performing the tasks of model-parameter estimation and annotation in TCRFs have been proposed. Experimental results indicate that the proposed TCRFs for hierarchical semantic annotation can significantly outperform the existing linear-chain CRF model.

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“…Bunescu et al [2] use Relational Markov Networks and model dependencies between distant entities. They apply special templates in order to assign equal labels if the text of the tokens is identical.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, much effort went in new approaches that can be summarized under the term Collective IE [2,4,8,9,15]. They break the linearchain assumption and model also long-range dependencies in order to label related entities or instances collectively.…”

Section: Introductionmentioning

confidence: 99%

Collective Information Extraction with Context-Specific Consistencies

Kluegl

Toepfer

Lemmerich

et al. 2012

Machine Learning and Knowledge Discovery in Databases

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Abstract. Conditional Random Fields (CRFs) have been widely used for information extraction from free texts as well as from semi-structured documents. Interesting entities in semi-structured domains are often consistently structured within a certain context or document. However, their actual compositions vary and are possibly inconsistent among different contexts. We present two collective information extraction approaches based on CRFs for exploiting these context-specific consistencies. The first approach extends linear-chain CRFs by additional factors specified by a classifier, which learns such consistencies during inference. In a second extended approach, we propose a variant of skip-chain CRFs, which enables the model to transfer long-range evidence about the consistency of the entities. The practical relevance of the presented work for real-world information extraction systems is highlighted in an empirical study. Both approaches achieve a considerable error reduction.

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Collective information extraction with relational Markov networks

Cited by 82 publications

References 10 publications

Assessing the Impact of Single and Pairwise Slot Constraints in a Factor Graph Model for Template-Based Information Extraction

Assessing the Impact of Single and Pairwise Slot Constraints in a Factor Graph Model for Template-Based Information Extraction

Tree-Structured Conditional Random Fields for Semantic Annotation

Collective Information Extraction with Context-Specific Consistencies

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