Scalable Disambiguation System Capturing Individualities of Mentions

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Title: Scalable Disambiguation System Capturing Individualities of Mentions
Authors: Mai, Tiep
Shi, Bichen
Nicholson, Patrick K.
Ajwani, Deepak
Sala, Alessandra
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Date: 27-May-2017
Online since: 2019-04-10T11:33:54Z
Abstract: Entity disambiguation, or mapping a phrase to its canonical representation in a knowledge base, is a fundamental step in many natural language processing applications. Existing techniques based on global ranking models fail to capture the individual peculiarities of the words and hence, struggle to meet the accuracy-time requirements of many real-world applications. In this paper, we propose a new system that learns specialized features and models for disambiguating each ambiguous phrase in the English language. We train and validate the hundreds of thousands of learning models for this purpose using a Wikipedia hyperlink dataset with more than 170 million labelled annotations. The computationally intensive training required for this approach can be distributed over a cluster. In addition, our approach supports fast queries, efficient updates and its accuracy compares favorably with respect to other state-of-the-art disambiguation systems.
Type of material: Conference Publication
Publisher: Springer
Start page: 365
End page: 379
Series/Report no.: Lecture Notes in Computer Science (volume 10318)
Copyright (published version): 2017 Springer
Keywords: Entity linkingEntity disambiguationWikificationWord-sense disambiguation
DOI: 10.1007/978-3-319-59888-8_31
Language: en
Status of Item: Not peer reviewed
Is part of: Gracia, J., Bond, F., McCrae, F. et al. (eds.). Language, Data, and Knowledge: First International Conference, LDK 2017, Galway, Ireland, June 19-20, 2017, Proceedings
Conference Details: Language, Data, and Knowledge - First International Conference (LDK 2017), Galway, Ireland, 19-20 June 2017
ISBN: 9783319598871
Appears in Collections:Computer Science Research Collection

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