Abstract
The subject of conversational mining has become of great interest recently due to the explosion of social and other online media. Supplementing this explosion of text is the advancement in pre-trained language models which have helped us to leverage these sources of information. An interesting domain to analyse is conversations in terms of complexity and value. Complexity arises due to the fact that a conversation can be asynchronous and can involve multiple parties. It is also computationally intensive to process. We use unsupervised methods in our work in order to develop a conversational pattern mining technique which does not require time consuming, knowledge demanding and resource intensive labelling exercises. The task of identifying repeating patterns in sequences is well researched in the Bioinformatics field. In our work, we adapt this to the field of Natural Language Processing and make several extensions to a motif detection algorithm. In order to demonstrate the application of the algorithm on a dynamic, real world data set; we extract motifs from an open-source film script data source. We run an exploratory investigation into the types of motifs we are able to mine.
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The authors want to acknowledge the contribution of ABSA bank which sponsors the Data Science Chair.
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Garber, N., Marivate, V. (2023). Conversational Pattern Mining Using Motif Detection. In: Ngatched Nkouatchah, T.M., Woungang, I., Tapamo, JR., Viriri, S. (eds) Pan-African Artificial Intelligence and Smart Systems. PAAISS 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-031-25271-6_22
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