论文信息 - Statistical Methods for Word Association in Text Mining

Statistical Methods for Word Association in Text Mining

Text data has been growing dramatically in the last years, mainly due to the advance of web related technologies that enable people to produce an overwhelming amount of data. Many knowledge about the world is encoded in text data available through blogs, tweets, web pages, articles, and books.

M. Filomena Teodoro | Anacleto Correia | Victor Lobo

[1] David M. Blei,et al. Probabilistic topic models , 2012, Commun. ACM.

[2] John Elder,et al. Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications , 2012 .

[3] ChengXiang Zhai,et al. Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining , 2016 .

[4] Yue Lu,et al. Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA , 2011, Information Retrieval.

[5] Hinrich Schütze,et al. Introduction to information retrieval , 2008 .

[6] Michael W. Berry,et al. Survey of Text Mining , 2003, Springer New York.

[7] ChengXiang Zhai,et al. Random walks on adjacency graphs for mining lexical relations from big text data , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[8] Michael W. Berry,et al. Survey of Text Mining II , 2008 .

[9] Thomas M. Cover,et al. Elements of Information Theory , 2005 .

[10] Lillian Lee,et al. Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[11] Andreas Hotho,et al. A Brief Survey of Text Mining , 2005, LDV Forum.

[12] Michael W. Berry,et al. Survey of Text Mining: Clustering, Classification, and Retrieval , 2007 .

[13] Hinrich Schütze,et al. Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.