Pierre Zweigenbaum

Publications (3)5.2 Total impact

• Chapter: Advanced Literature-Mining Tools

  Pierre Zweigenbaum, Dina Demner-Fushman
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  ABSTRACT: The complexity and wide range of current biomedical research is reflected in the number and scope of biomedical publications. Due to this abundance scientists are often no longer capable of keeping up with publications in their specific areas of research, let alone finding, reading, and analyzing potentially related scientific publications. Real advances in research, however, can be achieved only if a researcher can obtain an overview of the state of a given research question in a timely manner. This chapter presents methods to help researchers access the content of the biomedical literature. Information Retrieval (IR) identifies, in a large document database, the documents that are most relevant to a search topic provided by a user. Natural Language Processing (NLP) affords finer-grained access to more precise information contained in texts, which opens up a range of data analysis and knowledge synthesis functionalities. Powerful tools have been designed to exploit these techniques for the benefit of biomedical researchers, extracting millions of facts from the published literature and assisting Literature-Based Discovery. This chapter is organized as follows. It first describes the current capacities of IR from the Medline® bibliographic database. A short introduction to the main concepts of Natural Language Processing follows. Tasks which build on Natural Language Processing are then presented: Information Extraction and its derivatives and Literature-Based Discovery. A review of some existing applications closes the chapter. The references cited in the text are supplemented by a list of textbooks and Web resources.
  09/2009: pages 347-380;
• Article: Frontiers of biomedical text mining: current progress.

  Pierre Zweigenbaum, Dina Demner-Fushman, Hong Yu, Kevin B Cohen
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  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.
  Briefings in Bioinformatics 10/2007; 8(5):358-75. · 5.20 Impact Factor
• Conference Proceeding: Session Introduction.

  Pierre Zweigenbaum, Dina Demner-Fushman, Hong Yu, K. Bretonnel Cohen
  Biocomputing 2007, Proceedings of the Pacific Symposium, Maui, Hawaii, USA, 3-7 January 2007; 01/2007