Extended natural language data base interactions

AbstractAn oft-heard argument for Natural Language interfaces is their promise of reducing the effort an infrequent user would have to exert in using a computer system. This viewpoint has led to a research concentration on removing “artificial” constraints on a user's freedom of expression, allowing it to move closer to everyday speech. This paper discusses two complementary directions for extending Natural Language interfaces to data bases, which can make them more useful systems. These extensions make Natural Language interfaces less simply “windows” through which data can be called into view, and more “articulate experts” on the data base system and what it represents. The two directions involve (1) broadening the range of query types that can be handled and (2) extending the range of responses that can be provided

Requirements engineering: a review and research agenda

This paper reviews the area of requirements engineering. It outlines the key concerns to which attention should be devoted by both practitioners, who wish to "reengineer" their development processes, and academics, seeking intellectual challenges. It presents an assessment of the state-of-the-art and draws conclusions in the form of a research agenda

An approach to the analysis and deisgn of an intelligent tutoring system using an object-oriented methodology

A true Intelligent Tutoring System is difficult to produce in today\u27s technological environment. This thesis reviews various theoretical methods and strategies that could be employed in performing the analysis and design of an Intelligent Tutoring System. An overview of the basic concepts of Object-Oriented Analysis and Design are provided in this thesis. The notation system provided by these concepts are utilized. The Object-Oriented Analysis and Design methods that are employed create a basis for an implementation of an Intelligent Tutoring System

Planning Responses From High-Level Goals: Adopting the Respondent\u27s Perspective Cooperative Response Generation

Within the natural-language research community it has long been acknowledged that the conventions and pragmatics of natural-language communication often oblige dialogue systems to consider and address the underlying purposes of queries in their responses rather than answering them literally and without further comment or elaboration. Such systems cannot simply translate their users\u27 requests into transactions on database or expert systems, but must apply many more complex reasoning mechanisms to the task of selecting responses that are both appropriate and useful. This idea has given rise to a broadly-defined program of research in cooperative response generation (CRG). Research in CRG carried on over more than a decade has yielded a substantial body of literature. Analysis of that literature, however, shows that investigators have focused primarily on modeling manifestations of cooperative behavior without directly considering the nature and motivations of the behavior itself. But if we want to develop natural language dialogue systems that are truly to function as cooperative respondents instead of serving only as models of particular kinds of cooperative responses, a different approach is required. I identify two opposing perspectives on the process of cooperative response generation: the questioner-based and the respondent-based perspectives. I argue that past research efforts have largely been questioner-based, and that this view has led to the development of theories that are incompatible and cannot be integrated. I propose the respondent-based view as an alternative, and provide evidence that taking such a perspective might allow several interesting but otherwise poorly-understood aspects of cooperative response behavior to be modeled. The final portion of the dissertation explores the computational implications of a respondent-based perspective. I outline the architecture of a Cooperative Response Planning System, a dialogue system that raises, reasons about, and attempts to satisfy high-level cooperative goals in its responses. This architecture constitutes a first approximation to a theory of how a system might reason from the beliefs it derives from a questioner\u27s utterances to choose a cooperative response. The processing of two sample responses in this framework is described in detail to illustrate the architecture\u27s capabilities

A workshop on the gathering of information for problem formulation

Issued as Quarterly progress reports no. [1-5], Proceedings and Final contract report, Project no. G-36-651Papers presented at the Workshop/Symposium on Human Computer Interaction, March 26 and 27, 1981, Atlanta, G

Developing an innovative entity extraction method for unstructured data

The main goal of this study is to build high-precision extractors for entities such as Person and Organization as a good initial seed that can be used for training and learning in machine-learning systems, for the same categories, other categories, and across domains, languages, and applications. The improvement of entities extraction precision also increases the relationships extraction precision, which is particularly important in certain domains (such as intelligence systems, social networking, genetic studies, healthcare, etc.). These increases in precision improve the end users’ experience quality in using the extraction system because it lowers the time that users spend for training the system and correcting outputs, focusing more on analyzing the information extracted to make better data-driven decisions

SuperMat: Construction of a linked annotated dataset from superconductors-related publications

A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) processes are still needed to efficiently access and exploit this accumulated knowledge, paving the way towards data-driven materials design. Herein, we present SuperMat (Superconductor Materials), an annotated corpus of linked data derived from scientific publications on superconductors, which comprises 142 articles, 16052 entities, and 1398 links that are characterised into six categories: the names, classes, and properties of materials; links to their respective superconducting critical temperature (Tc); and parametric conditions such as applied pressure or measurement methods. The construction of SuperMat resulted from a fruitful collaboration between computer scientists and material scientists, and its high quality is ensured through validation by domain experts. The quality of the annotation guidelines was ensured by satisfactory Inter Annotator Agreement (IAA) between the annotators and the domain experts. SuperMat includes the dataset, annotation guidelines, and annotation support tools that use automatic suggestions to help minimise human errors

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version