A question-answering machine learning system for FAQs

With the increase in usage and dependence on the internet for gathering information, it’s now essential to efficiently retrieve information according to users’ needs. Question Answering (QA) systems aim to fulfill this need by trying to provide the most relevant answer for a user’s query expressed in natural language text or speech. Virtual assistants like Apple Siri and automated FAQ systems have become very popular and with this the constant rush of developing an efficient, advanced and expedient QA system is reaching new limits. In the field of QA systems, this thesis addresses the problem of finding the FAQ question that is most similar to a user’s query. Finding semantic similarities between database question banks and natural language text is its foremost step. The work aims at exploring unsupervised approaches for measuring semantic similarities for developing a closed domain QA system. To meet this objective modern sentence representation techniques, such as BERT and FLAIR GloVe, are coupled with various similarity measures (cosine, Euclidean and Manhattan) to identify the best model. The developed models were tested with three FAQs and SemEval 2015 datasets for English language; the best results were obtained from the coupling of BERT embedding with Euclidean distance similarity measure with a performance of 85.956% on a FAQ dataset. The model is also tested for Portuguese language with Portuguese Health support phone line SNS24 dataset; Sumário: Um sistema de pergunta-resposta de aprendizagem automatica para FAQs Com o aumento da utilização e da dependência da internet para a recolha de informação, tornou-se essencial recuperar a informação de forma eficiente de acordo com as necessidades dos utilizadores. Os Sistemas de Pergunta- Resposta (PR) visam responder a essa necessidade, tentando fornecer a resposta mais relevante para a consulta de um utilizador expressa em texto em linguagem natural escrita ou falada. Os assistentes virtuais como o Apple Siri e sistemas automatizados de perguntas frequentes tornaram-se muito populares aumentando a necessidade de desenvolver um sistema de controle de qualidade eficiente, avançado e conveniente. No campo dos sistemas de PR, esta dissertação aborda o problema de encontrar a pergunta que mais se assemelha à consulta de um utilizador. Encontrar semelhanças semânticas entre a base de dados de perguntas e o texto em linguagem natural é a sua etapa mais importante. Neste sentido, esta dissertação tem como objetivo explorar abordagens não supervisionadas para medir similaridades semânticas para o desenvolvimento de um sistema de pergunta-resposta de domínio fechado. Neste sentido, técnicas modernas de representação de frases como o BERT e FLAIR GloVe são utilizadas em conjunto com várias medidas de similaridade (cosseno, Euclidiana e Manhattan) para identificar os melhores modelos. Os modelos desenvolvidos foram testados com conjuntos de dados de três FAQ e o SemEval 2015; os melhores resultados foram obtidos da combinação entre modelos de embedding BERT e a distância euclidiana, tendo-se obtido um desempenho máximo de 85,956% num conjunto de dados FAQ. O modelo também é testado para a língua portuguesa com o conjunto de dados SNS24 da linha telefónica de suporte de saúde em português

Bilateral Multi-Perspective Matching for Natural Language Sentences

Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model under the "matching-aggregation" framework. Given two sentences $P$ and $Q$, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions $P \rightarrow Q$ and $P \leftarrow Q$. In each matching direction, each time step of one sentence is matched against all time-steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fix-length matching vector. Finally, based on the matching vector, the decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks.Comment: To appear in Proceedings of IJCAI 201

SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology

In this era of knowledge economy in which knowledge have become the most precious resource, surveys have shown that e-Learning has been on the increasing trend in various organizations including, among others, education and corporate. The use of e-Learning is not only aim to acquire knowledge but also to maintain competitiveness and advantages for individuals or organizations. However, the early promise of e-Learning has yet to be fully realized, as it has been no more than a handout being published online, coupled with simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by Web 2.0 technology still hardly overcome common problem such as information overload and poor content aggregation in a highly increasing number of learning objects in an e-Learning Management System (LMS) environment. The aim of this research study is to exploit the Semantic Web (SW) and Knowledge Management (KM) technology; the two emerging and promising technology to enhance the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is the backbone of SW and KM is introduced for managing knowledge especially from learning object and developing automated question answering system (Aquas) with expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to develop the Ontology in this research work. The potential of SW and KM technology is identified in this research finding which will benefit e-Learning developer to develop e-Learning system especially with social constructivist pedagogical approach from the point of view of KM framework and SW environment. The (semi-) automatic ontological knowledge base construction system (SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically whilst the Aquas with expert locator has facilitated knowledge retrieval that encourages knowledge sharing in e-Learning environment. The experiment conducted has shown that the SAOKBCS can extract concept that is the main component of Ontology from text learning object with precision of 86.67%, thus saving the expert time and effort to build Ontology manually. Additionally the experiment on Aquas has shown that more than 80% of users are satisfied with answers provided by the system. The expert locator framework can also improve the performance of Aquas in the future usage. Keywords: semantic web aware – knowledge e-Learning Management System (SWAKMDLS), semi-automatic ontological knowledge base construction system (SAOKBCS), automated question answering system (Aquas), Ontology, expert locator