Data Driven Content Creation using Statistical and Natural Language Processing Techniques for Financial Domain

• URL: http://arxiv.org/abs/2109.02935v1
• Date: Tue, 7 Sep 2021 08:37:28 GMT
• Title: Data Driven Content Creation using Statistical and Natural Language Processing Techniques for Financial Domain
• Authors: Ankush Chopra, Prateek Nagwanshi, Sohom Ghosh
• Abstract summary: We propose a two-part framework where the first part describes methods to combine the information from different interaction channels like call, search, and chat. The second part of the framework focuses on extracting customer questions by analyzing interaction data sources.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the years customers' expectation of getting information instantaneously has given rise to the increased usage of channels like virtual assistants. Typically, customers try to get their questions answered by low-touch channels like search and virtual assistant first, before getting in touch with a live chat agent or the phone representative. Higher usage of these low-touch systems is a win-win for both customers and the organization since it enables organizations to attain a low cost of service while customers get served without delay. In this paper, we propose a two-part framework where the first part describes methods to combine the information from different interaction channels like call, search, and chat. We do this by summarizing (using a stacked Bi-LSTM network) the high-touch interaction channel data such as call and chat into short searchquery like customer intents and then creating an organically grown intent taxonomy from interaction data (using Hierarchical Agglomerative Clustering). The second part of the framework focuses on extracting customer questions by analyzing interaction data sources. It calculates similarity scores using TF-IDF and BERT(Devlin et al., 2019). It also maps these identified questions to the output of the first part of the framework using syntactic and semantic similarity.

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