VOX : an extensible natural language processor

VOX is a Natural Language Processor whose knowledge can be extended by interaction with a user.VOX consists of a text analyzer and an extensibility system that share a knowledge base. The extensibility system lets the user add vocabulary, concepts, phrases, events, and scenarios to the knowledge base. The analyzer uses information obtained in this way to understand previously unhandled text.The underlying knowledge representation of VOX, called Conceptual Grammar, has been developed to meet the severe requirements of extensibility. Conceptual Grammar uniformly represents syntactic and semantic information, and permits modular addition of knowledge

Problem solving as intelligent retrieval from distributed knowledge sources

Distributed computing in intelligent systems is investigated from a different perspective. From the viewpoint that problem solving can be viewed as intelligent knowledge retrieval, the use of distributed knowledge sources in intelligent systems is proposed

Physical Object Representation and Generalization: A Survey of Natural Language Processing Programs

This paper surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding. Five programs serve as case studies for guiding the course of the paper. Within the framework of describing each of these programs, seven other programs, ideas and theories that are relevant to the program in focus are presented. Our current work which integrates representation and generalization is also discussed

Inference processing and error recovery in sentence understanding

Solving the mysteries of human language understanding inevitably requires an answer to the question of how the language understander resolves ambiguity, for human language is certainly ambiguous. But ambiguity leads to choices between possible explanations, and choice opens the door for mistakes. Unless we are willing to believe that the human language understander always makes the correct choice, any explanation of ambiguity resolution must be considered incomplete if it does not also account for recovery from an incorrect decision.This dissertation describes a new approach to lexical ambiguity resolution during sentence understanding which is implemented in a program called ATLAST. Many computational models of natural language understanding have dealt with lexical ambiguity resolution, but ATLAST is one of the few models to address the associated problem of error recovery. ATLAST's ability to recover from an incorrect lexical inference decision stems from its ability to retain unchosen word meanings for a period of time after it selects the apparently context-appropriate meaning of an ambiguous word. The short-term retention of possible lexical inferences permits ATLAST to recover from incorrect decisions without backtracking and reprocessing text, and without keeping a record of possible choices indefinitely.The principle of retention provides a solution to the problem of error recovery which is compatible with current psycholinguistic theories of lexical disambiguation. Furthermore, the existence of some form of retention in lexical disambiguation is supported by the results of experiments with human subjects. This dissertation includes a discussion of these results and speculation on how the principle of retention might be extended to account for recovery from erroneous higher-level inference decisions

Representing Complex Physical Objects in Memory

Researchers an artificial intelligence have proposed and implemented several representation systems for use in computer programs that "understand" natural language input. Noticeably lacking from these systems is a robust and concise method of representing complex physical objects. This paper describes a language independent scheme for encoding real-world objects in a manner that captures elements of how people describe them. Two major groups of objects are distinguished: ''unitary'' objects that are described by a single "shape-descriptor"; and "composite" objects that are represented by a frame-based system that focuses on the physical relations that exist among obiects. The heart of this scheme is a primitive-based framework that classifies physical relations into three fundamental categories with five possible properties. Our current work on RESEARCHER, a program that employs this scheme while reading patent abstracts, is also discussed

An overview of computer-based natural language processing

Computer based Natural Language Processing (NLP) is the key to enabling humans and their computer based creations to interact with machines in natural language (like English, Japanese, German, etc., in contrast to formal computer languages). The doors that such an achievement can open have made this a major research area in Artificial Intelligence and Computational Linguistics. Commercial natural language interfaces to computers have recently entered the market and future looks bright for other applications as well. This report reviews the basic approaches to such systems, the techniques utilized, applications, the state of the art of the technology, issues and research requirements, the major participants and finally, future trends and expectations. It is anticipated that this report will prove useful to engineering and research managers, potential users, and others who will be affected by this field as it unfolds

Corpus-Based Techniques for Word Sense Disambiguation

The need for robust and easily extensible systems for word sense disambiguation coupled with successes in training systems for a variety of tasks using large on-line corpora has led to extensive research into corpus-based statistical approaches to this problem. Promising results have been achieved by vector space representations of context, clustering combined with a semantic knowledge base, and decision lists based on collocational relations. We evaluate these techniques with respect to three important criteria: how their definition of context affects their ability to incorporate different types of disambiguating information, how they define similarity among senses, and how easily they can generalize to new senses. The strengths and weaknesses of these systems provide guidance for future systems which must capture and model a variety of disambiguating information, both syntactic and semantic

ETRANS: A English-Thai translator

ETRANS is an experimental English-Thai machine translation (MT) system that translates a simple English sentence into a grammatically correct Thai sentence. The entire system is written in C-Prolog, and runs on UNIX systems. The MT strategy taken by ETRANS is an interlingual strategy with a parser for English and a generator for Thai. The parser creates a semantic representation equivalent to the meaning of the English sentence. A generator then interprets the semantic representation into Thai. ETRANS employs frames as a means for representing knowledge, and an augmented transition network (ATN) as the linguistic framework for analyzing and generating sentences

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