Evaluating the helpfulness of linked entities to readers
|Evaluating the helpfulness of linked entities to readers|
|Author(s)||Yamada I., Ito T., Usami S., Takagi S., Takeda H., Takefuji Y.|
|Published in||HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media|
|Keyword(s)||entity linking, knowledge base, wikipedia (Extra: Knowledge based systems, Baseline methods, entity linking, Geographic location, Knowledge base, Machine-learning, User experience, Wikipedia, Hypertext systems)|
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Evaluating the helpfulness of linked entities to readers is a 2014 conference paper written in English by Yamada I., Ito T., Usami S., Takagi S., Takeda H., Takefuji Y. and published in HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media.
When we encounter an interesting entity (e.g., a person's name or a geographic location) while reading text, we typically search and retrieve relevant information about it. Entity linking (EL) is the task of linking entities in a text to the corresponding entries in a knowledge base, such as Wikipedia. Recently, EL has received considerable attention. EL can be used to enhance a user's text reading experience by streamlining the process of retrieving information on entities. Several EL methods have been proposed, though they tend to extract all of the entities in a document including unnecessary ones for users. Excessive linking of entities can be distracting and degrade the user experience. In this paper, we propose a new method for evaluating the helpfulness of linking entities to users. We address this task using supervised machine-learning with a broad set of features. Experimental results show that our method significantly outperforms baseline methods by approximately 5.7%-12% F1. In addition, we propose an application, Linkify, which enables developers to integrate EL easily into their web sites.
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