Topic Trees And Chatbot Conversational Context
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 8
Abstract
This article discusses the relation between topic-specific word trees (“ontologies”) and the retrieval of information from a chatbot‟s recent conversation – the conversational context. I assume the conversational context includes the sequence of recently visited nodes of the topic tree. So we caninterpret context retrieval as a problem of selection from this sequence, in reverse order. We will see that a surprisingly small set of selection methods support a diversity of indeterminate words and phrasesthat may need to be handled by a chatbot: like “it”, “them”, “what‟s the difference?”, “the third”, etc.An essential part of the solution is the way context is filteredto contain only the kinds of items we are expecting to find in the current input but that are missing. What is expected but missing drives context retrieval. In practice when an input sentence is encountered that is not understood and contains an indeterminate word, a chatbot may be allowed a second pass after retrieving and inserting recent context into the input.Theseideas are implemented in Python at: https://github.com/peterwaksman/Narwhal
Authors and Affiliations
Peter Waksman
Keywords
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- DOI 10.9790/9622-0808015662.
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