Intent classification through conversational interfaces : Classification within a small domain

Abstract: Natural language processing and Machine learning are subjects undergoing intense study nowadays. These fields are continually spreading, and are more interrelated than ever before. A case in point is text classification which is an instance of Machine learning(ML) application in Natural Language processing(NLP).Although these subjects have evolved over the recent years, they still have some problems that have to be considered. Some are related to the computing power techniques from these subjects require, whereas the others to how much training data they require.The research problem addressed in this thesis regards lack of knowledge on whether Machine learning techniques such as Word2Vec, Bidirectional encoder representations from transformers (BERT) and Support vector machine(SVM) classifier can be used for text classification, provided only a small training set. Furthermore, it is not known whether these techniques can be run on regular laptops.To solve the research problem, the main purpose of this thesis was to develop two separate conversational interfaces utilizing text classification techniques. These interfaces, provided with user input, can recognise the intent behind it, viz. classify the input sentence within a small set of pre-defined categories. Firstly, a conversational interface utilizing Word2Vec, and SVM classifier was developed. Secondly, an interface utilizing BERT and SVM classifier was developed. The goal of the thesis was to determine whether a small dataset can be used for intent classification and with what accuracy, and if it can be run on regular laptops.The research reported in this thesis followed a standard applied research method. The main purpose was achieved and the two conversational interfaces were developed. Regarding the conversational interface utilizing Word2Vec pre-trained dataset, and SVM classifier, the main results showed that it can be used for intent classification with the accuracy of 60%, and that it can be run on regular computers. Concerning the conversational interface utilizing BERT and SVM Classifier, the results showed that this interface cannot be trained and run on regular laptops. The training ran over 24 hours and then crashed.The results showed that it is possible to make a conversational interface which is able to classify intents provided only a small training set. However, due to the small training set, and consequently low accuracy, this conversational interface is not a suitable option for important tasks, but can be used for some non-critical classification tasks.