Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning

This paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include statistical, neural-network, decision-tree, rule-based, and case-based classification techniques. The specific problem tested involves disambiguating six senses of the word ``line'' using the words in the current and proceeding sentence as context. The statistical and neural-network methods perform the best on this particular problem and we discuss a potential reason for this observed difference. We also discuss the role of bias in machine learning and its importance in explaining performance differences observed on specific problems.Comment: 10 page

Content-Based Book Recommending Using Learning for Text Categorization

Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use social filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommended previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.Comment: 8 pages, 3 figures, Submission to Fourth ACM Conference on Digital Librarie

Learning Parse and Translation Decisions From Examples With Rich Context

We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state.Comment: 8 pages, LaTeX, 3 postscript figures, uses aclap.st

Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs

This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut. The method, called FOIDL, is based on FOIL (Quinlan, 1990) but employs intensional background knowledge and avoids the need for explicit negative examples. It is particularly useful for problems that involve rules with specific exceptions, such as learning the past-tense of English verbs, a task widely studied in the context of the symbolic/connectionist debate. FOIDL is able to learn concise, accurate programs for this problem from significantly fewer examples than previous methods (both connectionist and symbolic).Comment: See http://www.jair.org/ for any accompanying file

Health of children and young people in secure settings

This small-scale descriptive study was commissioned by the Children and Young People's Public Health team within the Department of Health, in partnership with Offender Health, in order to inform preparation and implementation of an Offender Health Strategy document for children and young people. The overall aim was to review what is currently known about healthcare for children and young people in the secure estate, covering all three types of settings (Young Offender Institution, Secure Training Centre and Secure Children's Home) and all aspects of health, but with a particular focus on physical health since more is already known about mental health and substance misuse among young people in secure settings

The prevalence and social distribution of domestic violence: an analysis of theory and method.

Domestic violence is recognised as an area that requires more detailed research, particularly on the general population. Indeed the lack of authoritative statistics on the extent of domestic violence is considered to restrict the development of preventative or remedial action to alleviate the problem. This thesis is concerned, therefore, with the development of a methodology in order to generate data on the incidence and prevalence of domestic violence, the relationship of this to current theory and the implications for policy. The main research component involved a victimisation survey adapted to deal with the specific problems of researching domestic violence. It utilized sensitive interviewing techniques, carefully worded questionnaires, a self-complete questionnaire (the 'piggy-back' method) and vignettes detailing 'conflict' situations which could lead to violence. 571 women and 429 men were interviewed which makes it the largest survey on domestic violence to be conducted in Great Britain. A qualitative component was additionally incorporated into the methodology in order to fully explore the experience of domestic violence. The primary focus of the research was on women's experiences of violence from husbands and boyfriends, including ex-partners, although additional information was collected on other forms domestic of and non-domestic violence against both men and women. The project investigated the extent of domestic violence; its variation by subgroup; the nature, context and impact of the violence; definitions; levels and patterns of reporting to the various agencies and satisfaction with the response; the relationship of domestic to stranger violence; the location of domestic violence and non-domestic violence and the gendered distribution of violence. The examination of so many areas could not have been achieved without the use of a multiplicity of methods. This thesis, however, deals not only with the development of methodology and the subsequent findings arising from the research project. It also analyses four major criminological theories (classicism, including the new administrative criminology; positivism; feminism and left realism) in relation to domestic violence. It delineates the main principles of each theory, details how it attempts to explain, research and tackle domestic violence and identifies both strengths and weaknesses. Furthermore, the empirical data generated by the research project enables the testing of hypotheses derived from the theoretical literature about the nature of violence, particularly with respect to its social and spatial patterning. On examination, the approaches of radical feminism and left realism are singled out as having the greatest purchase on the Phenomenon and a synthesis of these positions is demarcated: a feminist realism within criminology. Finally, both the research findings and theoretical discussion inform the policy recommendations. Both long-term and short-term initiatives are considered and an emphasis is placed on development of policy that is both multiagency and woman-centred