TY - GEN
T1 - Chinese organization name recognition based on multiple features
AU - Ling, Yajuan
AU - Yang, Jing
AU - He, Liang
PY - 2012
Y1 - 2012
N2 - Recognition of Chinese organization names is the key of the recognition of Chinese named entities. However, the lack of a single unified naming system to capture all types of organizations and the uncertainty in word segmentation, make the recognition of Chinese organization names especially difficult. In this paper, we focus on the recognition of Chinese organization names and propose an approach that takes advantage of various types of features of Chinese organization names to address it. First of all, we pre-process inputs to make the recognition more convenient. Secondly, we use the features of the left and right boundary to determine the candidate Chinese organization names automatically. Thirdly, we evaluate and refine the initial recognition results with the features of behaviors and debugging structure patterns to improve the performance of the recognition. From the experimental results on People's Daily testing data set, the approach proposed in this paper outperforms the method based on role tagging more than 7%. And through designing a series of other experiments, we have proved that the proposed approach can perfectly complete the task of recognizing Chinese organization names and is particularly effective in nested cases.
AB - Recognition of Chinese organization names is the key of the recognition of Chinese named entities. However, the lack of a single unified naming system to capture all types of organizations and the uncertainty in word segmentation, make the recognition of Chinese organization names especially difficult. In this paper, we focus on the recognition of Chinese organization names and propose an approach that takes advantage of various types of features of Chinese organization names to address it. First of all, we pre-process inputs to make the recognition more convenient. Secondly, we use the features of the left and right boundary to determine the candidate Chinese organization names automatically. Thirdly, we evaluate and refine the initial recognition results with the features of behaviors and debugging structure patterns to improve the performance of the recognition. From the experimental results on People's Daily testing data set, the approach proposed in this paper outperforms the method based on role tagging more than 7%. And through designing a series of other experiments, we have proved that the proposed approach can perfectly complete the task of recognizing Chinese organization names and is particularly effective in nested cases.
KW - Behavior feature
KW - Chinese organization name recognition
KW - Core feature word
KW - Debugging structure patterns
KW - Leftbounder rule
UR - https://www.scopus.com/pages/publications/84862208804
U2 - 10.1007/978-3-642-30428-6_11
DO - 10.1007/978-3-642-30428-6_11
M3 - 会议稿件
AN - SCOPUS:84862208804
SN - 9783642304279
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 136
EP - 144
BT - Intelligence and Security Informatics - Pacific Asia Workshop, PAISI 2012, Proceedings
T2 - Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2012
Y2 - 29 May 2012 through 29 May 2012
ER -