Author/Authors :
Wu, Chunguo Ministry of Education - Key Laboratory of Symbol Computation and Knowledge Engineering, China , Wu, Chunguo Railway Ministry - The Key Laboratory of Advanced Information Science and Network Technology of Beijing - The Key Laboratory of Information Science Engineering, China , Wu, Chunguo Jiaotong University, China , Wu, Chunguo Jiaotong University, China , Wu, Chunguo Jilin University - College of Computer Science and Technology, China , Marchese, Maurizio University of Trento - Department of Information and Communication Technology, Italy , Jiang, Jingqing Jilin University - College of Computer Science and Technology, China , Jiang, Jingqing Inner Mongolia University for Nationalities - College of Mathematics and Computer Science, China , Jiang, Jingqing Ministry of Education - Key Laboratory of Symbol Computation and Knowledge Engineering, China , Ivanyukovich, Alexander University of Trento - Department of Information and Communication Technology, Italy , Liang, Yanchun Jilin University - College of Computer Science and Technology, China , Liang, Yanchun Ministry of Education - Key Laboratory of Symbol Computation and Knowledge Engineering, China
Abstract :
Abstract: With the currently growing interest in the Semantic Web, keywords/metad- ata extraction is coming to play an increasingly important role. Keywords extraction from documents is a complex task in natural languages processing. Ideally this task con- cerns sophisticated semantic analysis. However, the complexity of the problem makes current semantic analysis techniques insufficient. Machine learning methods can sup- port the initial phases of keywords extraction and can thus improve the input to further semantic analysis phases. In this paper we propose a machine learning-based keywords extraction for given documents domain, namely scientific literature. More specifically, the least square support vector machine is used as a machine learning method. The proposed method takes the advantages of machine learning techniques and moves the complexity of the task to the process of learning from appropriate samples obtained within a domain. Preliminary experiments show that the proposed method is capable to extract keywords from the domain of scientific literature with promising results.
Keywords :
keywords extraction , metadata extraction , support vector machine , machine learning
