Entity Identification on Microblogs by CRF Model with Adaptive Dependency

Jun-Li LU
Makoto P. KATO
Katsumi TANAKA

IEICE TRANSACTIONS on Information and Systems   Vol.E99-D    No.9    pp.2295-2305
Publication Date: 2016/09/01
Publicized: 2016/06/20
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDP7015
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
entity identification,  indirect reference,  Conditional Random Field,  

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We address the problem of entity identification on a microblog with special attention to indirect reference cases in which entities are not referred to by their names. Most studies on identifying entities referred to them by their full/partial name or abbreviation, while there are many indirectly mentioned entities in microblogs, which are difficult to identify in short text such as microblogs. We therefore tackled indirect reference cases by developing features that are particularly important for certain types of indirect references and modeling dependency among referred entities by a Conditional Random Field (CRF) model. In addition, we model non-sequential order dependency while keeping the inference tractable by dynamically building dependency among entities. The experimental results suggest that our features were effective for indirect references, and our CRF model with adaptive dependency was robust even when there were multiple mentions in a microblog and achieved the same high performance as that with the fully connected CRF model.