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Sophia Ananiadou - One of the best experts on this subject based on the ideXlab platform.

  • Text Mining resources for the life sciences
    Database - The journal of Biological Databases and Curation, 2016
    Co-Authors: Piotr Przybyła, John Mcnaught, Matthew Shardlow, Sophie Aubin, Robert Bossy, Richard Eckart De Castilho, Stelios Piperidis, Sophia Ananiadou
    Abstract:

    Text Mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current Text Mining resources. In this survey, we give an overview of the Text Mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with Text Mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for Text Mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable Text Mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable—those that have the crucial ability to share information, enabling smooth integration and reusability.

  • Text Mining meets workflow
    Bioinformatics (Oxford England), 2010
    Co-Authors: Yoshinobu Kano, Paul D. Dobson, Mio Nakanishi, Jun'ichi Tsujii, Sophia Ananiadou
    Abstract:

    Summary: Text Mining from the biomedical literature is of increasing importance, yet it is not easy for the bioinformatics community to create and run Text Mining workflows due to the lack of accessibility and interoperability of the Text Mining resources. The U-Compare system provides a wide range of bio Text Mining resources in a highly interoperable workflow environment where workflows can very easily be created, executed, evaluated and visualized without coding. We have linked U-Compare to Taverna, a generic workflow system, to expose Text Mining functionality to the bioinformatics community. Availability: http://u-compare.org/taverna.html, http://u-compare.org Contact: kano@is.s.u-tokyo.ac.jp Supplementary information:Supplementary data are available at Bioinformatics online.

  • Supporting Systematic Reviews Using Text Mining
    Social Science Computer Review, 2009
    Co-Authors: Sophia Ananiadou, Brian Rea, Naoaki Okazaki, Rob Procter, James Thomas
    Abstract:

    In this article, we describe how we are using Text Mining solutions to enhance the production of systematic reviews. The aims of this collaborative project are the development of a Text Mining framework to support systematic reviews and the provision of a service exemplar serving as a test bed for deriving requirements for the development of more generally applicable Text Mining tools and services.

  • Text Mining for biology and biomedicine
    Unknown, 2005
    Co-Authors: Sophia Ananiadou, John Mcnaught
    Abstract:

    Introduction to Text Mining for Biology. Levels of Natural Language Processing for Text Mining. Lexical, Terminological and Ontological Resources For Biological Text Mining. Automatic Terminology Management in Biomedicine. Abbreviations in Biomedical Text. Named Entity Recognition. Information Extraction. Corpora and their Annotation. Evaluation of Text Mining in Biology. Integrating Text Mining with Data Mining.

  • Information Retrieval in Biomedicine - Text Mining for Biomedicine
    Information Retrieval in Biomedicine, 1
    Co-Authors: Sophia Ananiadou
    Abstract:

    Text Mining provides the automated means to manage information overload and overlook. By adding meaning to Text, Text Mining techniques produce a much more structured analysis of Textual knowledge than do simple word searches, and can provide powerful tools for knowledge discovery in biomedicine. In this chapter, the author focus on the Text Mining services for biomedicine offered by the United Kingdom National Centre for Text Mining.

Ingo Feinerer - One of the best experts on this subject based on the ideXlab platform.

  • A Text Mining framework in R and its applications
    2008
    Co-Authors: Ingo Feinerer
    Abstract:

    Text Mining has become an established discipline both in research as in business intelligence. However, many existing Text Mining toolkits lack easy extensibility and provide only poor support for interacting with statistical computing environments. Therefore we propose a Text Mining framework for the statistical computing environment R which provides intelligent methods for corpora handling, meta data management, preprocessing, operations on documents, and data export. We present how well established Text Mining techniques can be applied in our framework and show how common Text Mining tasks can be performed utilizing our infrastructure. The second part in this thesis is dedicated to a set of realistic applications using our framework. The first application deals with the implementation of a sophisticated mailing list analysis, whereas the second example identifies the potential of Text Mining methods for business to consumer electronic commerce. The third application shows the benefits of Text Mining for law documents. Finally we present an application which deals with authorship attribution on the famous Wizard of Oz book series. (author's abstract)

  • Text Mining Infrastructure in R
    Journal Of Statistical Software, 2008
    Co-Authors: Ingo Feinerer, Kurt Hornik, David Meyer
    Abstract:

    During the last decade Text Mining has become a widely used discipline utilizing sta- tistical and machine learning methods. We present the tm package which provides a framework for Text Mining applications within R. We give a survey on Text Mining facili- ties in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysismethods, Text clustering, Text classification and string kernels.

Yang Shu-qiang - One of the best experts on this subject based on the ideXlab platform.

  • Survey of Text Mining based on ontology
    Journal of Computer Applications, 2006
    Co-Authors: Yang Shu-qiang
    Abstract:

    Text Mining is an effective means of detecting potentially useful knowledge from large Text database.However,conventional Text Mining technology cannot achieve high accuracy,because it cannot effectively make use of the semantic information of the Text.Ontology provides theoretical basis and technical support for semantic information representation and organization.This paper introduces common ontology and domain ontology,and analyzes Text Mining technology based on these ontologies.

Zhang Guo-xuan - One of the best experts on this subject based on the ideXlab platform.

  • Study on the Text Mining and Chinese Text Mining Framework
    Information Sciences, 2007
    Co-Authors: Zhang Guo-xuan
    Abstract:

    Text Mining,also known as Text data Mining or knowledge discovery in Texts,focuses on computerized exploration of large amounts of Text and on discovery of implicit,previously unknown,and potentially useful patterns within them.Firstly,the Text Mining are introduced including its definition,its characteristics and its progress.Then,The problems and research direction of Chinese Text Mining are pointed out based on analysis for state-of-the-art of research on Chinese Text Mining.Finally,Unified Chinese Text Mining Framework(UCTMF) is presented.The framework are hierarchical,open,and scalable.It provide a unified and public frame for Chinese Text Mining System.

K. Bretonnel Cohen - One of the best experts on this subject based on the ideXlab platform.

  • Text Mining for the biocuration workflow
    2012
    Co-Authors: Lynette Hirschman, Martin Krallinger, Cecilia Arighi, Karen G. Dowell, Anália Lourenço, Gully A.p.c. Burns, K. Bretonnel Cohen, Eva Huala, Anne-lise Veuthey, Thomas C Wiegers, Robert Nash, Andrew Chatr-aryamontri, Alfonso Valencia, Cathy H. Wu, Andrew G. Winter
    Abstract:

    Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and Text Mining. However, to date, there have been few documented successes for improving biocuration throughput using Text Mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full Text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with Text Mining and almost half were using Text Mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of Text Mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, Text Mining developers and the larger biomedical research community.

  • Frontiers of biomedical Text Mining: current progress
    Proceedings - IEEE International Conference on Data Mining, ICDM, 2007
    Co-Authors: Pierre Zweigenbaum, Dina Demner-fushman, Huafeng Yu, Hong Yu, K. Bretonnel Cohen
    Abstract:

    It is now almost 15 years since the publication of the first paper on Text Mining in the genomics domain, and decades since the first paper on Text Mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in Text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical Text Mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical Text Mining or BioNLP' in general, focusing primarily on papers published within the past year. 10.1093/bib/bbm045