Identifying protein interaction abstracts with contextual bag of words

Authors:
Hsi-Chuan Hung;Richard Tzong-Han Tsai;Wen-Lian Hsu
Affiliations:
Institute of Information Science, Academia Sinica and Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, R.O.C.;Institute of Information Science, Academia Sinica, Taipei, Taiwan, R.O.C.;Institute of Information Science, Academia Sinica, Taipei, Taiwan, R.O.C.
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Year:
2007

Citing 4
Cited 0

Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Partially Supervised Classification of Text Documents

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Feature Engineering for Text Classification

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Filtering contents with bigrams and named entities to improve text classification

AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we focus on the identification of biomedical abstracts related to protein-protein interactions. We propose a novel feature representation, contextual-bag-of-words, to exploit named entity information. Our method outperforms well-known methods that use named entity information as additional features. Furthermore, we have improved the performance by extracting reliable and informative instances from unlabeled and likely-positive data to provide additional training data.