The SENSEI Project: Making Sense of Human Conversations

Conversational interaction is the most natural and persistent paradigm for personal and business relations. In contact centres customer spoken conversations are handled daily. On social media platforms conversations are delivered in different forms, lengths and for different purposes. In both cases, conversations have little impact on the intended target listeners, due to the volume, velocity and diversity (media, style, social context) of the document streams (spoken conversations and blog posts). Most language analytics technology is limited in that it performs keyword search, which does not provide automatic descriptions of what happened, who said what, which opinions are held on what subject, in a coherent, readable and executable form. In the SENSEI project we plan to go beyond keyword search and sentence-based analysis of conversations. We adapt lightweight and large coverage linguistic models of semantic and discourse resources to learn a layered model of conversations. SENSEI addresses the issue of multidimensional textual, spoken and metadata descriptors in terms of semantic, para-semantic and discourse structures. Automated generation of readable analytics documents (summaries) will support end-users in the context of large data analysis tasks. Summarization technology developed in SENSEI has been evaluated with respect to users’ task requirements and performances in the context of contact centre and social media conversations.