Mention detection in coreference resolution: survey

Abstract Motivation Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events’ attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results In this paper, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. Supplementary information Supplementary data are available at Bioinformatics online.

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Approaches to biomedical coreference resolution

Proceedings of the 7th ACM IKDD CoDS and 25th COMAD ◽

10.1145/3371158.3371217 ◽

2020 ◽

Author(s):

Ishani Mondal

Keyword(s):

Coreference Resolution

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Chinese Coreference Resolution via Bidirectional LSTMs using Word and Token Level Representations

2020 16th International Conference on Computational Intelligence and Security (CIS) ◽

10.1109/cis52066.2020.00024 ◽

2020 ◽

Author(s):

Kun Ming

Keyword(s):

Coreference Resolution

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Evaluation-driven design of a robust coreference resolution system

Natural Language Engineering ◽

10.1017/s135132490300319x ◽

2003 ◽

Vol 9 (3) ◽

pp. 281-306 ◽

Cited By ~ 1

Author(s):

ANDREI POPESCU-BELIS

Keyword(s):

Working Memory ◽

Theoretical Analysis ◽

Coreference Resolution ◽

Knowledge Sources ◽

Narrative Texts ◽

Data Annotation ◽

Evaluation Measures ◽

Optimal Behavior ◽

Resolution System

In this paper, we describe a system for coreference resolution and emphasize the role of evaluation for its design. The goal of the system is to group referring expressions (identified beforehand in narrative texts) into sets of coreferring expressions that correspond to discourse entities. Several knowledge sources are distinguished, such as referential compatibility between a referring expression and a discourse entity, activation factors for discourse entities, size of working memory, or meta-rules for the creation of discourse entities. For each of them, the theoretical analysis of its relevance is compared to scores obtained through evaluation. After looping through all knowledge sources, an optimal behavior is chosen, then evaluated on test data. The paper also discusses evaluation measures as well as data annotation, and compares the present approach to others in the field.

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Entity Type Recognition for Heterogeneous Semantic Graphs

AI Magazine ◽

10.1609/aimag.v36i1.2569 ◽

2015 ◽

Vol 36 (1) ◽

pp. 75-86 ◽

Cited By ~ 4

Author(s):

Jennifer Sleeman ◽

Tim Finin ◽

Anupam Joshi

Keyword(s):

Machine Learning ◽

Background Knowledge ◽

Knowledge Bases ◽

Heterogeneous Data ◽

Unstructured Data ◽

Supervised Machine Learning ◽

Coreference Resolution ◽

Multiple Sources ◽

Fine Grained ◽

High Level

We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

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