A study of the use of natural language processing for conversational agents

Language is a mark of humanity and conscience, with the conversation (or dialogue) as one of the most fundamental manners of communication that we learn as children. Therefore one way to make a computer more attractive for interaction with users is through the use of natural language. Among the systems with some degree of language capabilities developed, the Eliza chatterbot is probably the first with a focus on dialogue. In order to make the interaction more interesting and useful to the user there are other approaches besides chatterbots, like conversational agents. These agents generally have, to some degree, properties like: a body (with cognitive states, including beliefs, desires and intentions or objectives); an interactive incorporation in the real or virtual world (including perception of events, communication, ability to manipulate the world and communicate with others); and behavior similar to a human (including affective abilities). This type of agents has been called by several terms, including animated agents or embedded conversational agents (ECA). A dialogue system has six basic components. (1) The speech recognition component is responsible for translating the user’s speech into text. (2) The Natural Language Understanding component produces a semantic representation suitable for dialogues, usually using grammars and ontologies. (3) The Task Manager chooses the concepts to be expressed to the user. (4) The Natural Language Generation component defines how to express these concepts in words. (5) The dialog manager controls the structure of the dialogue. (6) The synthesizer is responsible for translating the agents answer into speech. However, there is no consensus about the necessary resources for developing conversational agents and the difficulties involved (especially in resource-poor languages). This work focuses on the influence of natural language components (dialogue understander and manager) and analyses, in particular the use of parsing systems as part of developing conversational agents with more flexible language capabilities. This work analyses what kind of parsing resources contributes to conversational agents and discusses how to develop them targeting Portuguese, which is a resource-poor language. To do so we analyze approaches to the understanding of natural language, and identify parsing approaches that offer good performance, based on which we develop a prototype to evaluate the impact of using a parser in a conversational agent.linguagem é uma marca da humanidade e da consciência, sendo a conversação (ou diálogo) uma das maneiras de comunicacão mais fundamentais que aprendemos quando crianças. Por isso uma forma de fazer um computador mais atrativo para interação com usuários é usando linguagem natural. Dos sistemas com algum grau de capacidade de linguagem desenvolvidos, o chatterbot Eliza é, provavelmente, o primeiro sistema com foco em diálogo. Com o objetivo de tornar a interação mais interessante e útil para o usuário há outras aplicações alem de chatterbots, como agentes conversacionais. Estes agentes geralmente possuem, em algum grau, propriedades como: corpo (com estados cognitivos, incluindo crenças, desejos e intenções ou objetivos); incorporação interativa no mundo real ou virtual (incluindo percepções de eventos, comunicação, habilidade de manipular o mundo e comunicar com outros agentes); e comportamento similar ao humano (incluindo habilidades afetivas). Este tipo de agente tem sido chamado de diversos nomes como agentes animados ou agentes conversacionais incorporados. Um sistema de diálogo possui seis componentes básicos. (1) O componente de reconhecimento de fala que é responsável por traduzir a fala do usuário em texto. (2) O componente de entendimento de linguagem natural que produz uma representação semântica adequada para diálogos, normalmente utilizando gramáticas e ontologias. (3) O gerenciador de tarefa que escolhe os conceitos a serem expressos ao usuário. (4) O componente de geração de linguagem natural que define como expressar estes conceitos em palavras. (5) O gerenciador de diálogo controla a estrutura do diálogo. (6) O sintetizador de voz é responsável por traduzir a resposta do agente em fala. No entanto, não há consenso sobre os recursos necessários para desenvolver agentes conversacionais e a dificuldade envolvida nisso (especialmente em línguas com poucos recursos disponíveis). Este trabalho foca na influência dos componentes de linguagem natural (entendimento e gerência de diálogo) e analisa em especial o uso de sistemas de análise sintática (parser) como parte do desenvolvimento de agentes conversacionais com habilidades de linguagem mais flexível. Este trabalho analisa quais os recursos do analisador sintático contribuem para agentes conversacionais e aborda como os desenvolver, tendo como língua alvo o português (uma língua com poucos recursos disponíveis). Para isto, analisamos as abordagens de entendimento de linguagem natural e identificamos as abordagens de análise sintática que oferecem um bom desempenho. Baseados nesta análise, desenvolvemos um protótipo para avaliar o impacto do uso de analisador sintático em um agente conversacional

Multi-facet rating of online hotel reviews: issues, methods and experiments

Online product reviews are becoming increasingly popular, and are being used more and more frequently by consumers in order to choose among competing products. Tools that rank competing products in terms of the satisfaction of consumers that have purchased the product before, are thus also becoming popular. We tackle the problem of rating (i.e., attributing a numerical score of satisfaction to) consumer reviews based on their tex- tual content. In this work we focus on multi-facet rating of hotel reviews, i.e., on the case in which the review of a hotel must be rated several times, according to several aspects (e.g., cleanliness, dining facilities, centrality of location). We explore several aspects of the problem, including the vectorial representation of the text based on sentiment analysis, collocation analysis, and feature selection for ordinal-regression learning. We present the results of experiments conducted on a corpus of approximately 15,000 hotel reviews that we have crawled from a popular hotel review site

Application and comparison of different classification methods based on symptom analysis with traditional classification technique for breast cancer diagnosis

Novel approach for classification technique such as Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA) and Random Forest (RF) using factor or dichotomic variables has been introduced. This study searches for the highly informative finitely linear combinations (symptoms) of variables in the finite field on the based of the Fisher’s exact test and accurately predict the target class for each case in the data. There are several super symptoms have comparable p-values. In this case, it becomes possible to choose as a nominative representative the factor which is more accessible for interpretation. The super symptom means a linear combination of various multiplications of k dichotomous variables over a field of characteristic 2 without repeating. In algebra, such functions are called Zhegalkin polynomials or algebraic normal forms

Natural Language Syntax Complies with the Free-Energy Principle

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design - such as "minimal search" criteria from theoretical syntax - adhere to the FEP. This affords a greater degree of explanatory power to the FEP - with respect to higher language functions - and offers linguistics a grounding in first principles with respect to computability. We show how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel-Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing-Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference

Homes became the “everything space” during COVID-19: impact of changes to the home environment on children’s physical activity and sitting

BackgroundDuring the 2020 UK COVID-19 lockdown restrictions, children spent almost all of their time at home, which had a significant influence on their physical activity (PA) and sedentary behaviour. This study aimed to: 1) determine changes to the social and physical environment at home and children’s home-based sitting, PA, standing and sitting breaks as a result of the COVID-19 restrictions; and 2) examine associations between changes at home and children’s movement behaviours.MethodsOne hundred and two children had their PA and sitting, standing and sitting breaks at home objectively measured pre-COVID-19 and during the first COVID-19 lockdown (June-July 2020). Children’s parents (n = 101) completed an audit of their home physical environment and a survey on the home social environment at both time points. Changes in the home physical and social environment and behavioural outcomes were assessed using Wilcoxon signed ranked tests, paired t-tests, or chi-square. Repeated linear regression analyses examined associations between changes in homes and changes in the home-based behavioural outcomes.ResultsDuring COVID-19, households increased the amount of seated furniture and electronic media equipment at home. The number of books and PA equipment decreased and fewer parents enforced a screen-time rule. Children’s preference for physical activities and socialising at home decreased. Time at home and sitting at home increased during COVID-19, whilst PA, standing and sitting breaks decreased. Both MVPA and TPA were positively associated with child preference for PA, and negatively associated with attending school. Sitting was negatively associated with child preference for PA and child preference for socialising at home. Media equipment was negatively associated with sitting breaks, whilst PA equipment was positively associated with standing.ConclusionThe COVID-19 restrictions forced children to spend almost all their time at home. Children’s PA, standing, and sitting breaks at home declined during the restrictions, while sitting increased. Mostly negative changes occurred in homes, some of which impacted children’s behaviours at home. To avoid the changes persisting post-lockdown, interventions are needed to reset and promote children’s PA and discourage prolonged sitting time