Ranking and tagging bursty features in text streams with context language models
Wayne Xin ZHAO1,2(), Chen LIU3, Ji-Rong WEN1,2, Xiaoming LI4
1. School of Information, Renmin University of China, Beijing 100872, China 2. Beijing Key Laboratory of Big Data Management and Analysis Methods, Renmin University of China, Beijing 100872, China 3. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing 100144, China 4. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
Detecting and using bursty patterns to analyze text streams has been one of the fundamental approaches in many temporal text mining applications. So far, most existing studies have focused on developing methods to detect bursty features based purely on term frequency changes. Few have taken the semantic contexts of bursty features into consideration, and as a result the detected bursty features may not always be interesting and can be hard to interpret. In this article, we propose to model the contexts of bursty features using a language modeling approach. We propose two methods to estimate the context language models based on sentence-level context and document-level context.We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. We also use two example text mining applications to qualitatively demonstrate the usefulness of bursty feature ranking and tagging.
. [J]. Frontiers of Computer Science, 2017, 11(5): 852-862.
Wayne Xin ZHAO, Chen LIU, Ji-Rong WEN, Xiaoming LI. Ranking and tagging bursty features in text streams with context language models. Front. Comput. Sci., 2017, 11(5): 852-862.
VlachosM, MeekC, VagenaZ, Gunopulos D. Identifying similarities, periodicities and bursts for online search queries. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data. 2004, 131–142 https://doi.org/10.1145/1007568.1007586
3
FungG P C, YuJ X, YuP S, Lu H. Parameter free bursty events detection in text streams. In: Proceedings of the 31st International Conference on Very Large Data Bases. 2005, 181–192
4
HeQ, ChangK Y, LimE P. Analyzing feature trajectories for event detection. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2007, 207–214 https://doi.org/10.1145/1277741.1277779
5
HeQ, ChangK Y, LimE P, Zhang J. Bursty feature representation for clustering text streams. In: Proceedings of the 2007 SIAM Conference on Data Mining. 2007, 491–496 https://doi.org/10.1137/1.9781611972771.50
6
LappasT, AraiB, PlatakisM, Kotsakos D, GunopulosD . On burstiness-aware search for document sequences. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009, 477–486 https://doi.org/10.1145/1557019.1557075
7
FungG P C, YuX J, LiuH, Yu P S. Time-dependent event hierarchy construction. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and DataMining. 2007, 300–309 https://doi.org/10.1145/1281192.1281227
8
ParikhN, Sundaresan N. Scalable and near real-time burst detection from ecommerce queries. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008, 972–980 https://doi.org/10.1145/1401890.1402006
9
KumarR, NovakJ, RaghavanP, Tomkins A. On the bursty evolution of blogspace. In: Proceedings of the 12th International Conference on World Wide Web. 2003, 568–576 https://doi.org/10.1145/775152.775233
10
WangX H, ZhaiC X, HuX, SproatR. Mining correlated bursty topic patterns from coordinated text streams. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007, 784–793 https://doi.org/10.1145/1281192.1281276
11
JiangY L, LinC X, MeiQ Z. Context comparison of bursty events in web search and online media. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. 2010, 1077–1087
12
YaoJ J, CuiB, HuangY X, Jin X. Temporal and social context based burst detection from folksonomies. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010, 1474–1479
13
MeiQ Z, XinD, ChengH, Han J W, ZhaiC X . Generating semantic annotations for frequent patterns with context analysis. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, 337–346 https://doi.org/10.1145/1150402.1150441
14
MeiQ Z, ShenX H, ZhaiC X. Automatic labeling of multinomial topic models. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007, 490–499 https://doi.org/10.1145/1281192.1281246
BleiD M, NgA Y, JordanM I. Latent Dirichlet allocation. The Journal of Machine Learning Research, 2003, 3: 993–1022
17
ZhaiC, Lafferty J. Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the 10th International Conference on Information and Knowledge Management. 2001, 403–410 https://doi.org/10.1145/502585.502654
18
DempsterA P, LairdN M, RubinD B. Maximum likelihood from incomplete data via the EM algorithm.Journal of the Royal Statistical Society, 1917, 39(1): 1–38
