Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35672
Title: Web person disambiguation using hierarchical co-reference model
Authors: Xu, J
Lu, Q 
Li, ML
Li, WJ 
Issue Date: 2015
Publisher: Springer
Source: In A. Gelbukh (Ed.), Computational linguistics and intelligent text processing : 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings. Part I, p. 279-291. Cham : Springer, 2015 How to cite?
Series/Report no.: Lecture Notes in Computer Science ; v. 9041
Abstract: As one of the entity disambiguation tasks, Web Person Disambiguation (WPD) identifies different persons with the same name by grouping search results for different persons into different clusters. Most of current research works use clustering methods to conduct WPD. These approaches require the tuning of thresholds that are biased towards training data and may not work well for different datasets. In this paper, we propose a novel approach by using pairwise co-reference modeling for WPD without the need to do threshold tuning. Because person names are named entities, disambiguation of person names can use semantic measures using the so called co-reference resolution criterion across different documents. The algorithm first forms a forest with person names as observable leaf nodes. It then stochastically tries to form an entity hierarchy by merging names into a sub-tree as a latent entity group if they have co-referential relationship across documents. As the joining/partition of nodes is based on co-reference-based comparative values, our method is independent of training data, and thus parameter tuning is not required. Experiments show that this semantic based method has achieved comparable performance with the top two state-of-the-art systems without using any training data. The stochastic approach also makes our algorithm to exhibit near linear processing time much more efficient than HAC based clustering method. Because our model allows a small number of upper-level entity nodes to summarize a large number of name mentions, the model has much higher semantic representation power and it is much more scalable over large collections of name mentions compared to HAC based algorithms.
URI: http://hdl.handle.net/10397/35672
ISBN: 978-3-319-18111-0
978-3-319-18110-3
ISSN: 0302-9743
DOI: 10.1007/978-3-319-18111-0_22
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