746 research outputs found

    Automatic extraction of knowledge from web documents

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    A large amount of digital information available is written as text documents in the form of web pages, reports, papers, emails, etc. Extracting the knowledge of interest from such documents from multiple sources in a timely fashion is therefore crucial. This paper provides an update on the Artequakt system which uses natural language tools to automatically extract knowledge about artists from multiple documents based on a predefined ontology. The ontology represents the type and form of knowledge to extract. This knowledge is then used to generate tailored biographies. The information extraction process of Artequakt is detailed and evaluated in this paper

    Automatic text summarisation of case law using gate with annie and summa plug-ins

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    Legal reasoning and judicial verdicts in many legal systems are highly dependent on case law. The ever increasing number of case law make the task of comprehending case law in a legal case cumbersome for legal practitioners; and this invariably stifles their efficiency. Legal reasoning and judicial verdicts will therefore be easier and faster, if case law were in abridged form that preserves their original meaning. This paper used the General Information Extraction System Architecture approach and integrated Natural Language Processing, Annotation, and Information Extraction tools to develop a software system that does automatic extractive text summarisation of Nigeria Supreme Court case law. The summarised case law which were about 20% of their original, were evaluated for semantic preservation and has shown to be 83% reliable.Keywords: Case law, text summarisation, text engineering, text annotation, text extractio

    Web document summarisation: a task-oriented evaluation

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    We present a query-biased summarisation interface for Web searching. The summarisation system has been specifically developed to act as a component in existing Web search interfaces. The summaries allow the user to more effectively assess the content of Web pages. We also present an experimental investigation of this approach. Our experimental results shows the system appears to be more useful and effective in helping users gauge document relevance than the traditional ranked titles/abstracts approach

    Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations

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    Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processing text documents related to the legal domain. In this study, we developed a mechanism to classify the relationships that can be observed among sentences in transcripts of United States court cases. First, we defined relationship types that can be observed between sentences in court case transcripts. Then we classified pairs of sentences according to the relationship type by combining a machine learning model and a rule-based approach. The results obtained through our system were evaluated using human judges. To the best of our knowledge, this is the first study where discourse relationships between sentences have been used to determine relationships among sentences in legal court case transcripts.Comment: Conference: 2018 International Conference on Advances in ICT for Emerging Regions (ICTer

    Users' perception of relevance of spoken documents

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    We present the results of a study of user's perception of relevance of documents. The aim is to study experimentally how users' perception varies depending on the form that retrieved documents are presented. Documents retrieved in response to a query are presented to users in a variety of ways, from full text to a machine spoken query-biased automatically-generated summary, and the difference in users' perception of relevance is studied. The experimental results suggest that the effectiveness of advanced multimedia information retrieval applications may be affected by the low level of users' perception of relevance of retrieved documents

    Summarisation and visualisation of e-Health data repositories

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    At the centre of the Clinical e-Science Framework (CLEF) project is a repository of well organised, detailed clinical histories, encoded as data that will be available for use in clinical care and in-silico medical experiments. We describe a system that we have developed as part of the CLEF project, to perform the task of generating a diverse range of textual and graphical summaries of a patientā€™s clinical history from a data-encoded model, a chronicle, representing the record of the patientā€™s medical history. Although the focus of our current work is on cancer patients, the approach we describe is generalisable to a wide range of medical areas

    Extractive text summarisation using graph triangle counting approach: proposed method

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    Currently, with a growing quantity of automated text data, the necessity for the con-struction of Summarisation systems turns out to be vital. Summarisation systems confine and condense the mainly vital ideas of the papers and assist the user to find and understand the foremost facts of the text quicker and easier from the dispensation of information. Compelling set of such systems are those that create summaries of ex-tracts. This type of summary, which is called Extractive Summarisation , is created by choosing large significant fragments of the text without making any amendment to the original. One methodology for generating this type of summary is consuming the graph theory. In graph theory there is one field called graph pruning / reduction, which means, to find the best representation of the main graph with a smaller number of nodes and edges. In this paper, a graph reduction technique called the triangle counting approach is presented to choose the most vital sentences of the text. The first phase is to represent a text as a graph, where nodes are the sentences and edges are the similarity between the sentences. The second phase is to construct the triangles, after that bit vector representation and the final phase is to retrieve the sentences based on the values of bit vector
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