An Entity Based Model for Coreference Resolution

Wick, Michael; Culotta, Aron; Rohanimanesh, Khashayar; McCallum, Andrew

doi:10.1137/1.9781611972795.32

Cited by 31 publications

(23 citation statements)

References 15 publications

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“…In this section, we introduce the concept of factor graphs [17], following the notations in [29] and [16]. A factor graph G is a bipartite graph that defines a probability distribution π.…”

Section: Imperatively Defined Factor Graphsmentioning

confidence: 99%

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Combining Textual and Graph-Based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models

Hakimov

Horst

Jebbara

et al. 2016

Lecture Notes in Computer Science

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Abstract. Named Entity Disambiguation (NED) is the task of disambiguating named entities in a natural language text by linking them to their corresponding entities in a knowledge base such as DBpedia, which are already recognized. It is an important step in transforming unstructured text into structured knowledge. Previous work on this task has proven a strong impact of graph-based methods such as PageRank on entity disambiguation. Other approaches rely on distributional similarity between an article and the textual description of a candidate entity. However, the combined impact of these different feature groups has not been explored to a sufficient extent. In this paper, we present a novel approach that exploits an undirected probabilistic model to combine different types of features for named entity disambiguation. Capitalizing on Markov Chain Monte Carlo sampling, our model is capable of exploiting complementary strengths between both graph-based and textual features. We analyze the impact of these features and their combination on named entity disambiguation. In an evaluation on the GERBIL benchmark, our model compares favourably to the current state-of-the-art in 8 out of 14 data sets.

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Section: Imperatively Defined Factor Graphsmentioning

confidence: 99%

“…For inference during training and testing, we rely on a Markov Chain Monte Carlo (MCMC) [2] approach. For training, we rely on the SampleRank [29] algorithm.…”

Section: Introductionmentioning

confidence: 99%

Combining Textual and Graph-Based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models

Hakimov

Horst

Jebbara

et al. 2016

Lecture Notes in Computer Science

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“…For example f (P ) = f (E1, E2, · · · , En) is defined for an entire partitioning and f (S) = f (E4, E9) is also defined for a subpartitioning consisting of only two entities (E4 and E9). This formulation of coreference encapsulates a number of existing coreference models, such as pairwise [10,5,13], entitywise [4,14], and hierarchical [15].…”

Section: Entity Resolutionmentioning

confidence: 99%

Assessing confidence of knowledge base content with an experimental study in entity resolution

Wick

Singh

Kobren

et al. 2013

Proceedings of the 2013 Workshop on Automated Knowledge Base Construction

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The purpose of this paper is to begin a conversation about the importance and role of confidence estimation in knowledge bases (KBs). KBs are never perfectly accurate, yet without confidence reporting their users are likely to treat them as if they were, possibly with serious real-world consequences. We define a notion of confidence based on the probability of a KB fact being true. For automatically constructed KBs we propose several algorithms for estimating this confidence from pre-existing probabilistic models of data integration and KB construction. In particular, this paper focuses on confidence estimation in entity resolution. A goal of our exposition here is to encourage creators and curators of KBs to include confidence estimates for entities and relations in their KBs.

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“…For example, to identify whether the author names of some research papers refer to the same person, it is often not sufficient to resolve the name and looking at the venue, title, and co-authorship relations [32]. Determining the identity of resources is often referred to as entity resolution [32], coreference resolution [50], object identification [40], and canonicalization [51,50]. The task of determining the identity on the Web becomes more and more important as more datasets appear and is a significant hurdle for providing large scale Semantic Web applications [21].…”

Section: Identity and Alignmentmentioning

confidence: 99%

The Semantic Web: Collective Intelligence on the Web

Janik

Scherp

Staab

2011

Informatik Spektrum

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Abstract. The World Wide Web has turned hypertext into a success story by enabling world-wide sharing of unstructured information and informal knowledge. The Semantic Web targets the sharing of structured information and formal knowledge pursuing objectives of achieving collective intelligence on the Web. Germane to the structure of the Semantic Web is a layering and standardization of concerns. These concerns are reflected by an architecture of the Semantic Web that we present through a common use case. Semantic Web data for the use case is now found on the Web and is part of a quickly growing set of Semantic Web resources available for formal processing.

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An Entity Based Model for Coreference Resolution

Cited by 31 publications

References 15 publications

Combining Textual and Graph-Based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models

Combining Textual and Graph-Based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models

Assessing confidence of knowledge base content with an experimental study in entity resolution

The Semantic Web: Collective Intelligence on the Web

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