Title Named Entity Recognition using Wikipedia and Abbreviation Generation

被引:0
作者
Park, Youngmin [1 ]
Kang, Sangwoo [1 ]
Seo, Jungyun [1 ]
机构
[1] Sogang Univ, Dept Comp Sci & Engn, Seoul, South Korea
来源
2014 INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP) | 2014年
关键词
Title named entity; Wikipedia; Conditional random field; Abbreviation generation;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we propose a title named entity recognition model using Wikipedia and abbreviation generation. The proposed title named entity recognition model automatically extracts title named entities from Wikipedia so constant renewal is possible without additional costs. Also, in order to establish a dictionary of title named entity abbreviations, generation rules are used to generate abbreviation candidates and abbreviations are selected through web search methods. In this paper, we propose a statistical model that recognizes title named entities using CRFs (Conditional Random Fields). The proposed model uses lexical information, a named entity dictionary, and an abbreviation dictionary, and provides title named entity recognition performance of 82.1% according to experimental results.
引用
收藏
页码:169 / 172
页数:4
相关论文
共 50 条
[11]   A Comparative Study of Named Entity Recognition for Telugu [J].
Gorla, SaiKiranmai ;
Murthy, N. L. Bhanu ;
Malapati, Aruna .
PROCEEDINGS OF THE 9TH ANNUAL MEETING OF THE FORUM FOR INFORMATION RETRIEVAL EVALUATION (FIRE 2017), 2017, :21-24
[12]   A Finnish news corpus for named entity recognition [J].
Teemu Ruokolainen ;
Pekka Kauppinen ;
Miikka Silfverberg ;
Krister Lindén .
Language Resources and Evaluation, 2020, 54 :247-272
[13]   Improved Deep Persian Named Entity Recognition [J].
Bokaei, Mohammad Hadi ;
Mahmoudi, Maryam .
2018 9TH INTERNATIONAL SYMPOSIUM ON TELECOMMUNICATIONS (IST), 2018, :381-386
[14]   A Finnish news corpus for named entity recognition [J].
Ruokolainen, Teemu ;
Kauppinen, Pekka ;
Silfverberg, Miikka ;
Linden, Krister .
LANGUAGE RESOURCES AND EVALUATION, 2020, 54 (01) :247-272
[15]   Enhancement of Medical Named Entity Recognition Using Graph-based Features [J].
Keretna, Sara ;
Lim, Chee Peng ;
Creighton, Doug .
2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS, 2015, :1895-1900
[16]   Using Non-Local Features to Improve Named Entity Recognition Recall [J].
Mao, Xinnian ;
Xu, Wei ;
Dong, Yuan ;
He, Saike ;
Wang, Haila .
PACLIC 21: THE 21ST PACIFIC ASIA CONFERENCE ON LANGUAGE, INFORMATION AND COMPUTATION, PROCEEDINGS, 2007, :303-+
[17]   A Joint Neural Model for Fine-Grained Named Entity Classification of Wikipedia Articles [J].
Suzuki, Masatoshi ;
Matsuda, Koji ;
Sekine, Satoshi ;
Okazaki, Naoaki ;
Inui, Kentaro .
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2018, E101D (01) :73-81
[18]   Research on the Extraction of Wikipedia-Based Chinese-Khmer Named Entity Equivalents [J].
Xia, Qing ;
Yan, Xin ;
Yu, Zhengtao ;
Gao, Shengxiang .
NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2015, 2015, 9362 :372-379
[19]   TwiNER: Named Entity Recognition in Targeted Twitter Stream [J].
Li, Chenliang ;
Weng, Jianshu ;
He, Qi ;
Yao, Yuxia ;
Datta, Anwitaman ;
Sun, Aixin ;
Lee, Bu-Sung .
SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 2012, :721-730
[20]   Cyrillic Mongolian Named Entity Recognition with Rich Features [J].
Wang, Weihua ;
Bao, Feilong ;
Gao, Guanglai .
NATURAL LANGUAGE UNDERSTANDING AND INTELLIGENT APPLICATIONS (NLPCC 2016), 2016, 10102 :497-505