Dirichlet process mixtures model based on variational inference for Chinese person name disambiguation

Qi Zhou; Yang Liu; Yuliang Wei; Wei Wang; Bailing Wang; Siyan Wu

doi:10.1145/3219788.3219803

Dirichlet process mixtures model based on variational inference for Chinese person name disambiguation

Qi Zhou
, Yang Liu
, Yuliang Wei
, Wei Wang
, Bailing Wang
, Siyan Wu

School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Person name ambiguity in Web search results is a very common phenomenon. Although many methods have been proposed for solving the problem of person name ambiguity, their accuracy still must be enhanced in the complex and heterogeneous webpages. We introduce a variational inference algorithm for the Dirichlet process mixtures model (DPMM) for text clustering to disambiguate person name. Experiments on web data from different search engines indicate that our approach consistently outperforms other clustering methods such as K-means clustering and agglomerative hierarchical clustering.

Original language	English
Title of host publication	ICCDE 2018 - International Conference on Computing and Data Engineering
Publisher	Association for Computing Machinery
Pages	6-10
Number of pages	5
ISBN (Print)	9781450363938
DOIs	https://doi.org/10.1145/3219788.3219803
State	Published - 4 May 2018
Externally published	Yes
Event	2018 International Conference on Computing and Data Engineering, ICCDE 2018 - Shanghai, China Duration: 4 May 2018 → 6 May 2018

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F137704

Conference

Conference	2018 International Conference on Computing and Data Engineering, ICCDE 2018
Country/Territory	China
City	Shanghai
Period	4/05/18 → 6/05/18

Keywords

Agglomerative hierarchical clustering
Dirichlet process mixtures model
K-means clustering
Person name disambiguation
Variational inference

Access to Document

10.1145/3219788.3219803

Cite this

Zhou, Q., Liu, Y., Wei, Y., Wang, W., Wang, B., & Wu, S. (2018). Dirichlet process mixtures model based on variational inference for Chinese person name disambiguation. In ICCDE 2018 - International Conference on Computing and Data Engineering (pp. 6-10). (ACM International Conference Proceeding Series; Vol. Part F137704). Association for Computing Machinery . https://doi.org/10.1145/3219788.3219803

@inproceedings{86abdf60fddb47ff8449f9f0adb54624,

title = "Dirichlet process mixtures model based on variational inference for Chinese person name disambiguation",

abstract = "Person name ambiguity in Web search results is a very common phenomenon. Although many methods have been proposed for solving the problem of person name ambiguity, their accuracy still must be enhanced in the complex and heterogeneous webpages. We introduce a variational inference algorithm for the Dirichlet process mixtures model (DPMM) for text clustering to disambiguate person name. Experiments on web data from different search engines indicate that our approach consistently outperforms other clustering methods such as K-means clustering and agglomerative hierarchical clustering.",

keywords = "Agglomerative hierarchical clustering, Dirichlet process mixtures model, K-means clustering, Person name disambiguation, Variational inference",

author = "Qi Zhou and Yang Liu and Yuliang Wei and Wei Wang and Bailing Wang and Siyan Wu",

note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 2018 International Conference on Computing and Data Engineering, ICCDE 2018 ; Conference date: 04-05-2018 Through 06-05-2018",

year = "2018",

month = may,

day = "4",

doi = "10.1145/3219788.3219803",

language = "英语",

isbn = "9781450363938",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery ",

pages = "6--10",

booktitle = "ICCDE 2018 - International Conference on Computing and Data Engineering",

address = "美国",

}

Zhou, Q, Liu, Y , Wei, Y , Wang, W , Wang, B & Wu, S 2018, Dirichlet process mixtures model based on variational inference for Chinese person name disambiguation. in ICCDE 2018 - International Conference on Computing and Data Engineering. ACM International Conference Proceeding Series, vol. Part F137704, Association for Computing Machinery , pp. 6-10, 2018 International Conference on Computing and Data Engineering, ICCDE 2018, Shanghai, China, 4/05/18. https://doi.org/10.1145/3219788.3219803

Dirichlet process mixtures model based on variational inference for Chinese person name disambiguation. / Zhou, Qi; Liu, Yang ; Wei, Yuliang et al.
ICCDE 2018 - International Conference on Computing and Data Engineering. Association for Computing Machinery , 2018. p. 6-10 (ACM International Conference Proceeding Series; Vol. Part F137704).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Dirichlet process mixtures model based on variational inference for Chinese person name disambiguation

AU - Zhou, Qi

AU - Liu, Yang

AU - Wei, Yuliang

AU - Wang, Wei

AU - Wang, Bailing

AU - Wu, Siyan

PY - 2018/5/4

Y1 - 2018/5/4

N2 - Person name ambiguity in Web search results is a very common phenomenon. Although many methods have been proposed for solving the problem of person name ambiguity, their accuracy still must be enhanced in the complex and heterogeneous webpages. We introduce a variational inference algorithm for the Dirichlet process mixtures model (DPMM) for text clustering to disambiguate person name. Experiments on web data from different search engines indicate that our approach consistently outperforms other clustering methods such as K-means clustering and agglomerative hierarchical clustering.

AB - Person name ambiguity in Web search results is a very common phenomenon. Although many methods have been proposed for solving the problem of person name ambiguity, their accuracy still must be enhanced in the complex and heterogeneous webpages. We introduce a variational inference algorithm for the Dirichlet process mixtures model (DPMM) for text clustering to disambiguate person name. Experiments on web data from different search engines indicate that our approach consistently outperforms other clustering methods such as K-means clustering and agglomerative hierarchical clustering.

KW - Agglomerative hierarchical clustering

KW - Dirichlet process mixtures model

KW - K-means clustering

KW - Person name disambiguation

KW - Variational inference

UR - https://www.scopus.com/pages/publications/85053705950

U2 - 10.1145/3219788.3219803

DO - 10.1145/3219788.3219803

M3 - 会议稿件

AN - SCOPUS:85053705950

SN - 9781450363938

T3 - ACM International Conference Proceeding Series

SP - 6

EP - 10

BT - ICCDE 2018 - International Conference on Computing and Data Engineering

PB - Association for Computing Machinery

T2 - 2018 International Conference on Computing and Data Engineering, ICCDE 2018

Y2 - 4 May 2018 through 6 May 2018

ER -

Dirichlet process mixtures model based on variational inference for Chinese person name disambiguation

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Keywords

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