NDLI: Summarizing Entity Descriptions for Effective and Efficient Human-centered Entity Linking

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Summarizing Entity Descriptions for Effective and Efficient Human-centered Entity Linking

Content Provider	ACM Digital Library
Author	Xu, Danyun Qu, Yuzhong Cheng, Gong
Abstract	Entity linking connects the Web of documents with knowledge bases. It is the task of linking an entity mention in text to its corresponding entity in a knowledge base. Whereas a large body of work has been devoted to automatically generating candidate entities, or ranking and choosing from them, manual efforts are still needed, e.g., for defining gold-standard links for evaluating automatic approaches, and for improving the quality of links in crowdsourcing approaches. However, structured descriptions of entities in knowledge bases are sometimes very long. To avoid overloading human users with too much information and help them more efficiently choose an entity from candidates, we aim to substitute entire entity descriptions with compact, equally effective structured summaries that are automatically generated. To achieve it, our approach analyzes entity descriptions in the knowledge base and the context of entity mention from multiple perspectives, including characterizing and differentiating power, information overlap, and relevance to context. Extrinsic evaluation (where human users carry out entity linking tasks) and intrinsic evaluation (where human users rate summaries) demonstrate that summaries generated by our approach help human users carry out entity linking tasks more efficiently (22-23% faster), without significantly affecting the quality of links obtained; and our approach outperforms existing approaches to summarizing entity descriptions.
Starting Page	184
Ending Page	194
Page Count	11
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741094
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Quadratic multidimensional knapsack Entity summarization Class vector model Human-centered entity linking
Content Type	Text
Resource Type	Article

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