Hello & Goodbye: Conversation Boundary Identification Using Text Classification

One of the main challenges in discourse analysis is the process of segmenting text into meaningful topic segments. While this problem has been studied over the past thirty years, previous topic segmentation studies ignore crucial elements of a conversation: an opening and closing remark. Our motivation to revisit this problem space is the rise of instant message usage. We consider the problem of topic segmentation as a machine learning classification one. Using both enterprise and open source datasets, we address the question as to whether a machine learning algorithm can be trained to identify salutations and valedictions within multi-party real-time chat conversations. Our results show that both Naïve Bayes (NB) and Support Vector Machine (SVM) algorithms provide a reasonable degree of precision(mean F1 score: 0.58).

[1]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[2]  John D. Lafferty,et al.  Statistical Models for Text Segmentation , 1999, Machine Learning.

[3]  Li Liao,et al.  Combining Pairwise Sequence Similarity and Support Vector Machines for Detecting Remote Protein Evolutionary and Structural Relationships , 2003, J. Comput. Biol..

[4]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[5]  Susan T. Dumais,et al.  A Bayesian Approach to Filtering Junk E-Mail , 1998, AAAI 1998.

[6]  Hideki Kozima,et al.  Text Segmentation Based on Similarity between Words , 1993, ACL.

[7]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[8]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Laurence Likforman-Sulem,et al.  Text line segmentation of historical documents: a survey , 2007, International Journal of Document Analysis and Recognition (IJDAR).

[10]  Taylor Jackson Scott,et al.  Statistical affect detection in collaborative chat , 2013, CSCW.

[11]  H. B. Barlow,et al.  Unsupervised Learning , 1989, Neural Computation.

[12]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[13]  Philip Resnik,et al.  SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations , 2012, ACL.

[14]  Eric Fosler-Lussier,et al.  Discourse Segmentation of Multi-Party Conversation , 2003, ACL.

[15]  Susan Conrad,et al.  Corpus Linguistics: Investigating Language Structure and Use , 1998 .

[16]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[17]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[18]  Arthur L. Samuel,et al.  Some studies in machine learning using the game of checkers , 2000, IBM J. Res. Dev..

[19]  Graeme Hirst,et al.  Lexical Cohesion Computed by Thesaural relations as an indicator of the structure of text , 1991, CL.

[20]  David W. Aha,et al.  Artificial Intelligence , 2014 .

[21]  Jeffrey C. Reynar.  An Automatic Method of Finding Topic Boundaries , 1994, ACL.