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|Title:||Linking fine-grained locations in user comments||Authors:||Han, Jialong
Zhao, Wayne Xin
Phan, Minh C.
Named Entity Recognition
DRNTU::Engineering::Computer science and engineering
|Issue Date:||2017||Source:||Han, J., Sun, A., Cong, G., Zhao, W. X., Ji, Z., & Phan, M. C. (2018). Linking fine-grained locations in user comments. IEEE Transactions on Knowledge and Data Engineering, 30(1), 59-72. doi:10.1109/TKDE.2017.2758780||Series/Report no.:||IEEE Transactions on Knowledge and Data Engineering||Abstract:||Many domain-specific websites host a profile page for each entity (e.g., locations on Foursquare, movies on IMDb, and products on Amazon) for users to post comments on. When commenting on an entity, users often mention other entities for reference or comparison. Compared with web pages and tweets, the problem of disambiguating the mentioned entities in user comments has not received much attention. This paper investigates linking fine-grained locations in Foursquare comments. We demonstrate that the focal location, i.e., the location that a comment is posted on, provides rich contexts for the linking task. To exploit such information, we represent the Foursquare data in a graph, which includes locations, comments, and their relations. A probabilistic model named FocalLink is proposed to estimate the probability that a user mentions a location when commenting on a focal location, by following different kinds of relations. Experimental results show that FocalLink is consistently superior under different collective linking settings.||URI:||https://hdl.handle.net/10356/86191
|ISSN:||1041-4347||DOI:||10.1109/TKDE.2017.2758780||Rights:||© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TKDE.2017.2758780.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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