Context identification of sentences in related work sections using a conditional random field: towards intelligent digital libraries
Angrosh, M. A.; Cranefield, Stephen; Stanger, Nigel
Cite this item:
Angrosh, M. A., Cranefield, S., & Stanger, N. (2010). Context identification of sentences in related work sections using a conditional random field: towards intelligent digital libraries. In J. Hunter, C. Lagoze, L. Giles, & Y.-F. Li (Eds.), JCDL ’10 Proceedings of the 10th Annual Joint Conference on Digital Libraries (pp. 293–302). Joint conference on digital libraries presented at the 10th annual joint conference on digital libraries, ACM. doi:10.1145/1816123.1816168
Permanent link to OUR Archive version:
http://hdl.handle.net/10523/7134
Abstract:
Identification of contexts associated with sentences is becoming increasingly necessary for developing intelligent information retrieval systems. This article describes a supervised learning mechanism employing a conditional random field (CRF) for context identification and sentence classification. Specifically, we focus on sentences in related work sections in research articles. Based on a generic rhetorical pattern, a framework for modelling the sequential flow in these sections is proposed. Adopting a generalization strategy, each of these sentences is transformed into a set of features, which forms our dataset. We distinguish between two kinds of features for each of these sentences viz., citation features and sentence features. While an overall accuracy of 96.51% is achieved by using a combination of both citation and sentence features, the use of sentence features alone yields an accuracy of 93.22%. The results also show F-Scores ranging from 0.99 to 0.90 for various classes indicating the robustness of our application.
Date:
2010
Editor:
Hunter, Jane; Lagoze, Carl; Giles, Lee; Li, Yuan-Fang
Publisher:
ACM
Pages:
293-302
Conference:
10th annual joint conference on digital libraries, Gold Coast, Queensland, Australia
Series number:
10
Rights Statement:
ACM New York, NY, USA ©2010
Keywords:
Sentence Classification; Citation Classification; Conditional Random Fields
Research Type:
Conference or Workshop Item (Paper published in proceedings)
Languages:
English