@inproceedings{9e240c7f9d494cab8506aead51c1c375,
title = "Random walks on adjacency graphs for mining lexical relations from big text data",
abstract = "Lexical relations, or semantic relations of words, are useful knowledge fundamental to all applications since they help to capture inherent semantic variations of vocabulary in human languages. Discovering such knowledge in a robust way from arbitrary text data is a significant challenge in big text data mining. In this paper, we propose a novel general probabilistic approach based on random walks on word adjacency graphs to systematically mine two fundamental and complementary lexical relations, i.e., paradigmatic and syntagmatic relations between words from arbitrary text data. We show that representing text data as an adjacency graph opens up many opportunities to define interesting random walks for mining lexical relation patterns, and propose specific random walk algorithms for mining paradigmatic and syntagmatic relations. Evaluation results on multiple corpora show that the proposed random walk-based algorithms can discover meaningful paradigmatic and syntagmatic relations of words from text data.",
author = "Shan Jiang and Chengxiang Zhai",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE International Conference on Big Data, IEEE Big Data 2014 ; Conference date: 27-10-2014 Through 30-10-2014",
year = "2014",
doi = "10.1109/BigData.2014.7004272",
language = "English (US)",
series = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "549--554",
editor = "Jimmy Lin and Jian Pei and Hu, {Xiaohua Tony} and Wo Chang and Raghunath Nambiar and Charu Aggarwal and Nick Cercone and Vasant Honavar and Jun Huan and Bamshad Mobasher and Saumyadipta Pyne",
booktitle = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
address = "United States",
}