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CALL FOR CHAPTERS
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Submission Deadline: November 1, 2007
Information Retrieval in Biomedicine: Natural Language Processing for
Knowledge Integration
A book edited by Pr. Violaine Prince and Dr Mathieu Roche, University of
Montpellier and LIRMM-CNRS, France
Introduction
There is nowadays an intense interest for bio natural language processing.
This field addresses the particular applications of natural language
processing (NLP) to biological and medical areas. Naming such a set of
applications denotes both the impact of NLP on the application domain. As
a feedback, the peculiarities of the later seems to have made NLP evolve
in a distinct and particular way.
Several articles and books chapters have been recently written on the
subject (Ibekwe-Sanjuan 2007, Ananiadou and McNaught 2006, Scherf et al.
2005, Cohen and Hersh 2005 are among the latest…). The issue they tackle
rises from the intensive research and publication activity in the medical
area. A bibliographical database such as Medline contains several millions
of articles and is thoroughly updated every day. Many medical researchers
and practitioners need to read papers not only in their discipline but in
other fields with which they have an interaction. For instance, cancer
specialists need to browse papers in oncogenetics, radiology, chemistry,
cellular biology, surgery and so forth.Every day new cross-studies are
published, and the medical community cannot cope with such a high rate of
information without being supported by automated or semi-automated tools
in Information Retrieval and Knowledge Integration.
According to Swanson’s pioneer work in the domain (Swanson 1986), the
abundant medical data could be used as a hypothesis generator for
orienting medical research. Since human operators cannot browse the huge
amount of information, he suggested that hidden correlations could be
automatically or semi-automatically found in this data, so as to suggest
new tracks for investigation. Nowadays, the most recent works in text
mining are able to suggest this type of scientific discovery: A recent
work by Chun et al. (2006) shows that mining Medline abstracts brought up
interesting topical relations between prostate cancer and genes. Beyond
medicine, it is the whole field of the “living sciences”, including all
facets of biology, that might benefit from text mining methods and
achievements (A recent paper by Ananiadou et al. (2006) describes
perspective actions of text mining in systems biology).
The Overall Objective of the Book
In the fields of bio NLP there exists a need for an edited collection of
articles in this area. Until now, the most intensively explored NLP areas
in biomedicine are those related to lexical knowledge and terminology.
Named entities recognition, abbreviations understanding and expansion,
terminological knowledge management have been largely addressed, with more
or less success. However, since NLP parsers are becoming more efficient,
and word-based approaches have reached their limits, new trends,
suggesting hybridation between linguistic knowledge and machine learning
or statistics-based algorithms are being seriously investigated.
The book aims to provide relevant theoretical frameworks and latest
empirical research findings in the area, according to a linguistic
granularity. At the lexical and terminological levels, it aims at
presenting original applications, going beyond the existing published
work. At the sentence level, it should present the latest achievements,
particularly by using NLP parsers. At the text/paragraph level, it is the
relationship with topics and pragmatics that opens the road for a broader
use of NLP in biomedicine. Moreover, two chapters will focus on aspects of
NLP which are becoming crucial: Evaluation and Innovative Software.
The Target Audience:
Professionals, PhD students and researchers working in the field of Text
Mining, BioNLP, Medical Sciences, and Computer-Assisted Medical
information systems. It is also relevant for computational linguists and
linguists who want to solve particular problems brought out by the
application domain. Moreover, the book will provide insights and support
executives concerned with the management of expertise, knowledge, and
information in health systems and biological textbases.
Recommended topics include the following:
Lexical-terminological level: Lexicology and terminology in BioNLP ; Using
BIO ontologies within a language context ; Updating ontologies in biology
or medicine with lexical knowledge
Sentence level: The question-answer approach in biomedicine; Operative
knowledge derived from NLP parsing and/or semantic representation
(application to biology and/or medicine); Approaches linking sentence
level with either terminology or segment level
Segment level: A topical and topic change approach to BioNLP (for
Information Retrieval or Knowledge Integration); Rhetorical structures,
scripts, or other models of this granularity; Approaches involving
language pragmatics in Biomedicine
Evaluation: Models or points of view in evaluating NLP approaches to
biomedicine
Innovative Software in BioNLP (short papers)
SUBMISSION PROCEDURE
Researchers and practitioners are invited to submit on or before
November 1, 2007, a 2-5 page manuscript proposal clearly explaining the
mission and concerns of the proposed chapter. Authors of accepted
proposals will be notified by December 1, 2007 about the status of their
proposals and sent chapter organizational guidelines. Full
chapters are expected to be submitted by March 15, 2008. All submitted
chapters will be reviewed on a double-blind review basis. The book is
scheduled to be published by IGI Global, www.igi-pub.com, publisher of the
IGI Publishing (formerly Idea Group Publishing), Information
Science Publishing, IRM Press, CyberTech Publishing and Information
Science Reference (formerly Idea Group Reference) imprints.
Inquiries and submissions can be forwarded electronically (Word
document) or by mail to:
Pr Violaine Prince
University of Montpellier 2 and LIRMM-CNRS
161 Ada Street F34392 Montpellier cedex 5
FRANCE
Tel.: +334 67 41 86 74 Fax: +334 67 41 85 00 GSM: +336 07 34 01 00
E-mail: prince@lirmm.fr