Building Knowledge Bases for the Generation of Software Documentation

Automated text generation requires a underlying knowledge base from which to generate, which is often difficult to produce. Software documentation is one domain in which parts of this knowledge base may be derived automatically. In this paper, we describe \drafter, an authoring support tool for generating user-centred software documentation, and in particular, we describe how parts of its required knowledge base can be obtained automatically.Comment: 6 pages, from COLING-9

Determining the Level of Expertise of a User of a Question Answering System

An intelligent question answering program should be able to tailor its answer to the user. Some factors entering this tailoring process include the level of expertise of the user, his goals for using the system, the discourse structure, and the user type. The decision on how sophisticated and detailed an answer should be is based, in part, on how much the user knows about the domain in question. In this paper, we are mainly concerned with determining the level of expertise of the user. We will show how a generalization based memory can be used in this process

Description strategies for naive and expert users

It is widely recognized that a question-answering system should be able to tailor its answers to the user. One of the dimensions along which this tailoring can occur is with respect to the level of knowledge of a user about a domain. In particular, responses should be different depending on whether they are addressed to naive or expert users. To understand what those differences should be, we analyzed texts from adult and junior encyclopedias. We found that two different strategies were used in describing complex physical objects to juniors and adults. We show how these strategies have been implemented on a test database

Explainable expert systems: A research program in information processing

Our work in Explainable Expert Systems (EES) had two goals: to extend and enhance the range of explanations that expert systems can offer, and to ease their maintenance and evolution. As suggested in our proposal, these goals are complementary because they place similar demands on the underlying architecture of the expert system: they both require the knowledge contained in a system to be explicitly represented, in a high-level declarative language and in a modular fashion. With these two goals in mind, the Explainable Expert Systems (EES) framework was designed to remedy limitations to explainability and evolvability that stem from related fundamental flaws in the underlying architecture of current expert systems

Extractive Summarisation of Medical Documents

Background Evidence Based Medicine (EBM) practice requires practitioners to extract evidence from published medical research when answering clinical queries. Due to the time-consuming nature of this practice, there is a strong motivation for systems that can automatically summarise medical documents and help practitioners find relevant information. Aim The aim of this work is to propose an automatic query-focused, extractive summarisation approach that selects informative sentences from medical documents. MethodWe use a corpus that is specifically designed for summarisation in the EBM domain. We use approximately half the corpus for deriving important statistics associated with the best possible extractive summaries. We take into account factors such as sentence position, length, sentence content, and the type of the query posed. Using the statistics from the first set, we evaluate our approach on a separate set. Evaluation of the qualities of the generated summaries is performed automatically using ROUGE, which is a popular tool for evaluating automatic summaries. Results Our summarisation approach outperforms all baselines (best baseline score: 0.1594; our score 0.1653). Further improvements are achieved when query types are taken into account. Conclusion The quality of extractive summarisation in the medical domain can be significantly improved by incorporating domain knowledge and statistics derived from a specialised corpus. Such techniques can therefore be applied for content selection in end-to-end summarisation systems

APPLYING ATTRIBUTION THEORY TO IS RESEARCH AS A PRACTICAL METHOD FOR ASSESSING POST-ADOPTION BEHAVIOUR

Researchers and practitioners alike see great valu in understanding the implementation, adoption, and use of technology, and acknowledge the need to better understand post-adoption behaviour. Among theories that explain and predict human behaviour, attribution theory is recognised for its extensive investigation of behaviour´s antecedents and consequnces. This paper provides an overview of the theory, suggests a practical method for using it in IS contexts, and shows evidence that this method provides meaningful results. In order to address the complexities encountered in field-work, this paper argus that system-usage can be treated as an interpersonal relationship between the user and the system. This perspective allows us to draw on extensive knowledge gained in the field of interpersonal relationships research, in particular a relationship diagnostics method that uses interview data, followed by an analysis of the attributions mentioned in the interviews. The paper provides evidence from a study that successfully used attribution theory in this way to investigate a non-interpersonal relationship “ an employee-organisation relationship. The paper concludes with suggestions for future research in IS based on this method