NDLI: Automatic Recognition of Chinese Organization Name Based on Conditional Random Fields

Content Provider	IEEE Xplore Digital Library
Author	Suxiang Zhang Suxian Zhang Xiaojie Wang
Copyright Year	2007
Description	Author affiliation: North China Electr. Power Univ., Baoding (Suxiang Zhang)
Abstract	Person, location and organization have been always mentioned as a bottleneck of a named entity recognition (NER) system. Automatic recognition of Chinese organization name is the most difficult problem in NER tasks. This paper presents a new approach of Chinese organization name recognition based on cascaded conditional random fields. In the proposed approach, we first recognize the person name and location name before recognizing organization. The model structure has been designed with the cascade way, the result then is passed to the high model and suppose the decision of high model for recognition of the complicated organization names. And we proposed the new feature to realize this task. We evaluate our approach on large-scale corpus with open test method using People's Daily (January. 1998). Chinese ORG recalling rate achieves 88.78% and the precision rate is 82.35%. The evaluation results show that our approach based on cascaded conditional random fields significantly outperforms previous approaches.
Starting Page	229
Ending Page	233
File Size	5048764
Page Count	5
File Format	PDF
ISBN	9781424416103
DOI	10.1109/NLPKE.2007.4368038
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text recognition Hidden Markov models Machine learning Educational institutions Large-scale systems Character recognition Data mining Sun Power engineering and energy Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Based cascaded conditional random fields model for Chinese Named Entity recognition

Chinese spoken language understanding with conditional random fields

Based Hybrid Method for Chinese Location Recognition

Chinese Named Entity Recognition with new contextual features

A New Machine Learning Method for Chinese Overlapping Disambiguity--Conditional Random Fields

Personal Name Recognition Based on Conditional Random Fields

Early results for Chinese named entity recognition using conditional random fields model, HMM and maximum entropy

Study on Affect of Part-of-Speech on the Performance of Chinese Named Entity Recognition Based on the Conditional Random Fields

Product features mining based on Conditional Random Fields model

Automatic Recognition of Chinese Organization Name Based on Conditional Random Fields

Similar Documents

Based cascaded conditional random fields model for Chinese Named Entity recognition

Chinese spoken language understanding with conditional random fields

Based Hybrid Method for Chinese Location Recognition

Chinese Named Entity Recognition with new contextual features

A New Machine Learning Method for Chinese Overlapping Disambiguity--Conditional Random Fields

Personal Name Recognition Based on Conditional Random Fields

Early results for Chinese named entity recognition using conditional random fields model, HMM and maximum entropy

Study on Affect of Part-of-Speech on the Performance of Chinese Named Entity Recognition Based on the Conditional Random Fields

Product features mining based on Conditional Random Fields model

Automatic Recognition of Chinese Organization Name Based on Conditional Random Fields