Person-specific named entity recognition using SVM with rich feature sets
NIE Hui; Nie Hui (
Source PublicationChinese Journal of Library and Information Science

Purpose: The purpose of the study is to explore the potential use of nature language process (NLP) and machine learning (ML) techniques and intents to find a feasible strategy and effective approach to fulfill the NER task for Web oriented person-specific information extraction.

Design/methodology/approach: An SVM-based multi-classification approach combined with a set of rich NLP features derived from state-of-the-art NLP techniques has been proposed to fulfill the NER task. A group of experiments has been designed to investigate the influence of various NLP-based features to the performance of the system, especially the semantic features. Optimal parameter settings regarding with SVM models, including kernel functions, margin parameter of SVM model and the context window size, have been explored through experiments as well.

Findings: The SVM-based multi-classification approach has been proved to be effective for the NER task. This work shows that NLP-based features are of great importance in datadriven NE recognition, particularly the semantic features. The study indicates that higher order kernel function may not be desirable for the specific classification problem in practical application. The simple linear-kernel SVM model performed better in this case. Moreover, the modified SVM models with uneven margin parameter are more common and flexible, which have been proved to solve the imbalanced data problem better.

Research limitations/implications: The SVM-based approach for NER problem is only proved to be effective on limited experiment data. Further research need to be conducted on the large batch of real Web data. In addition, the performance of the NER system need be tested when incorporated into a complete IE framework.

Originality/value: The specially designed experiments make it feasible to fully explore the characters of the data and obtain the optimal parameter settings for the NER task, leading to a preferable rate in recall, precision and F1 measures. The overall system performance (F1 value) for all types of name entities can achieve above 88.6%, which can meet the requirements for the practical application.

KeywordNamed Entity Recognition Natural Language Processing Svm-based Classifier Feature Selection
Subject Area编辑出版
Funding OrganizationThis work is support by the Special Research Fundation for Young Teachers of Sun Yat-sen University (Grant No. 2000-3161101) and Humanity and Social Science Youth Foundation of Ministry of Education of China (Grant No. 08JC870013).
Document Type期刊论文
CollectionJournal of Data and Information Science_Chinese Journal of Library and Information Science-2012
Corresponding AuthorNie Hui (
Recommended Citation
GB/T 7714
NIE Hui,Nie Hui . Person-specific named entity recognition using SVM with rich feature sets[J]. Chinese Journal of Library and Information Science,2012,5(3):27-46.
APA NIE Hui,&Nie Hui .(2012).Person-specific named entity recognition using SVM with rich feature sets.Chinese Journal of Library and Information Science,5(3),27-46.
MLA NIE Hui,et al."Person-specific named entity recognition using SVM with rich feature sets".Chinese Journal of Library and Information Science 5.3(2012):27-46.
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