Clustering of short sentences through representation of text data
Maxime Houé · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
Natural Language Processing has developed in the past few years very quickly.Numerous new applications emerged from new methods, notably involved bythe creation of the popular word embedding Word2Vec created by a team ofGoogle researchers. One of these new applications is the chatbot technology.The goal of these conversational interfaces is to be able to communicate automaticallywith humans via written or voice chat. With a chatbot, a companyhopes to improve customer relations at a lower cost. Unfortunately, the skills ofthe chatbots can vary a lot, and until now, their understanding of the humansis often rather bad. This harsh conclusion leads to wonder how the chatbotdevelopers can be helped for handling the large amounts of user requests notunderstood by their chatbot.This thesis was made in collaboration with a start-up named Askhub. Thisstart-up aims to help the companies with the development of their chatbot.The aim of this master thesis is to propose a clustering system in order toclassify the data not understood by a chatbot. To begin with, a study of thedierent methods of word embeddings has been realized, followed by a study ofdierent clustering techniques available suitable to the chosen word embedding.The results are then compared with some metrics and some propositions weremade in order to improve the clustering results.