Detecting citation types using finite-state machines

Authors:
Minh-Hoang Le;Tu-Bao Ho;Yoshiteru Nakamori
Affiliations:
School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan;School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan;School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

This paper presents a method to extract citation types from scientific articles, viewed as an intrinsic part of emerging trend detection (ETD) in scientific literature. There are two main contributions in this work: (1) Definition of six categories (types) of citations in the literature that are extractable, human-understandable, and appropriate for building the interest and utility functions in emerging trend detection models, and (2) A method to classify citation types using finite-state machines which does not require user-interactions or explicit knowledge. The experimental comparative evaluations show the high performance of the method and the proposed ETD model shows the crucial role of classified citation types in the detection of emerging trends in scientific literature.