Text messaging and retrieval techniques for a mobile health information system

Mobile phones have been identified as one of the technologies that can be used to overcome the challenges of information dissemination regarding serious diseases. Short message services, a much used function of cell phones, for example, can be turned into a major tool for accessing databases. This paper focuses on the design and development of a short message services-based information access algorithm to carefully screen information on human immunodeficiency virus/acquired immune deficiency syndrome within the context of a frequently asked questions system. However, automating the short message services-based information search and retrieval poses significant challenges because of the inherent noise in its communications. The developed algorithm was used to retrieve the best-ranked question–answer pair. Results were evaluated using three metrics: average precision, recall and computational time. The retrieval efficacy was measured and it was confirmed that there was a significant improvement in the results of the proposed algorithm when compared with similar retrieval algorithms

HITS and misses: combining BM25 with HITS for expert search

This paper describes the participation of Dublin City University in the CriES (Cross-Lingual Expert Search) pilot challenge. To realize expert search, we combine traditional information retrieval (IR)using the BM25 model with reranking of results using the HITS algorithm. The experiments were performed on two indexes, one containing all questions and one containing all answers. Two runs were submitted. The first one contains the combination of results from IR on the questions with authority values from HITS; the second contains the reranked results from IR on answers with authority values. To investigate the impact of multilinguality, additional experiments were conducted on the English topic subset and on all topics translated into English with Google Translate. The overall performance is moderate and leaves much room for improvement. However, reranking results with authority values from HITS typically improved results and more than doubled the number of relevant and retrieved results and precision at 10 documents in many experiments

Query Chains: Learning to Rank from Implicit Feedback

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.Comment: 10 page

Discovering semantic aspects of socially constructed knowledge hierarchy to boost the relevance of Web searching

The research intends to boost the relevance of Web search results by classifyingWebsnippet into socially constructed hierarchical search concepts, such as the mostcomprehensive human edited knowledge structure, the Open Directory Project (ODP). Thesemantic aspects of the search concepts (categories) in the socially constructed hierarchicalknowledge repositories are extracted from the associated textual information contributed bysocieties. The textual information is explored and analyzed to construct a category-documentset, which is subsequently employed to represent the semantics of the socially constructedsearch concepts. Simple API for XML (SAX), a component of JAXP (Java API for XMLProcessing) is utilized to read in and analyze the two RDF format ODP data files, structure.rdfand content.rdf. kNN, which is trained by the constructed category-document set, is used tocategorized the Web search results. The categorized Web search results are then ontologicallyfiltered based on the interactions of Web information seekers. Initial experimental resultsdemonstrate that the proposed approach can improve precision by 23.5%

Automatic Comprehension of Customer Queries for Feedback Generation

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg in fulfillment of the requirements for the degree of Master of Science, 2018One major challenge in customer-driven industries is the response to large volumes ofqueries. Inresponsetothisbusinessneed,FrequentlyAskedQuestions(FAQs)have been used for over four decades to provide customers with a repository of questions and associated answers. However, FAQs require some efforts on the part of the customers to search, especially when the FAQ repository is large and poorly indexed or structured. Thisevengetsdifficultwhenanorganisationhashundredsofqueriesinits repository of FAQs. One way of dealing with this rigorous task is to allow customers to ask their questions in a Natural Language, extract the meaning of the input text and automatically provide feedback from a pool of FAQs. This is an Information Retrieval (IR) problem, in Natural Language Processing (NLP). This research work, presents the first application of Jumping Finite Automata (JFA) — an abstract computing machine — in performing this IR task. This methodology involves the abstraction of all FAQs to a JFA and applying algorithms to map customer queries to the underlying JFA of all possible queries. A data set of FAQs from a university’s Computer and Network Service (CNS) was used as test case. A prototype chat-bot application was developed that takes customer queries in a chat, automatically maps them to a FAQ, and presents the corresponding answer to the user. This research is expected to be the first of such applications of JFA in comprehending customer queries.XL201

Chatbot development to assist patients in health care services

Dissertação de mestrado integrado em Engenharia InformáticaDados de alta qualidade sobre tratamentos médicos e de informação técnica tornaram-se acessíveis, criando novas oportunidades de E-Saúde para a recuperação de um paciente. A implementação da aprendizagem automática nestas soluções provou ser essencial e eficaz na elaboração de aplicações para o utilizador para aliviar a sobrecarga do sector de saúde. Atualmente, muitas interações com os utentes são realizadas via telefonemas e mensagens de texto. Os agentes de conversação podem responder a estas questões, fomentando uma rápida interação com os pacientes. O objetivo fundamental desta dissertação é prestar apoio aos pacientes, fornecendo uma fonte de informação fidedigna que lhes permita instruir-se e esclarecer dúvidas sobre os procedimentos e repercussões dos seus problemas de saúde. Este propósito foi concretizado não apenas através de uma plataforma Web intuitiva e acessível, composta por perguntas frequentes, mas também integrando um agente de conversação inteligente para responder a questões. Para este fim, cientificamente, foi necessário conduzir a investigação, implementação e viabilidade dos agentes de conversação no domínio fechado para os cuidados de saúde. Constitui um importante contributo para a comunidade de desenvolvimento de chatbots, na qual se reúnem as últimas inovações e descobertas, bem os desafios actuais da aprendizagem automática, contribuindo para a consciencialização desta área.High-quality data on medical treatments and facility-level information has become accessible, creating new eHealth opportunities for the recuperation of a patient. Machine learning implementation in these solutions has been proven to be essential and effective in building user-centred applications to relieves the burden on the healthcare sector. Nowadays, many patient interactions are handled through healthcare services via phone calls and text message exchange. Conversation agents can provide answers to these queries, promoting fast patient interaction. The underlying aim of this dissertation is to assist patients by providing a reliable source of information to educate themselves and clarify any doubts about procedures and implications of their health issue. This purpose was achieved not only through an intuitive and accessible web platform, with frequently asked questions, but also by integrating an intelligent chatting agent to answer questions. To this end, scientifically, it was necessary to conduct the research, implementation and feasibility of closed-domain conversation agents for healthcare. It is a valuable input for the chatbot development community, which assembles the latest innovations and findings, as well as the current challenges of machine learning, contributing to the awareness of this field