Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An annotation scheme for discourse-level argumentation in research articles
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
What's yours and what's mine: determining intellectual attribution in scientific text
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Table extraction using conditional random fields
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
International Journal of Intelligent Systems - Computational Models of Natural Argumentation
Towards multi-paper summarization reference information
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
An annotation scheme for citation function
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
Generalized isotonic conditional random fields
Machine Learning
The importance of fine-grained cue phrases in scientific citations
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Detecting citation types using finite-state machines
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Towards automated related work summarization
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Feature words that classify problem sentence in scientific article
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
AWC '13 Proceedings of the First Australasian Web Conference - Volume 144
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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.