Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration

Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration

Violaine Prince, Mathieu Roche

Copyright: © 2009 |Pages: 460

ISBN13: 9781605662749|ISBN10: 1605662747|EISBN13: 9781605662756|ISBN13 Softcover: 9781616925284

DOI: 10.4018/978-1-60566-274-9

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Prince, Violaine , and Mathieu Roche, editors. Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration. IGI Global, 2009. https://doi.org/10.4018/978-1-60566-274-9

APA

Prince, V. & Roche, M. (Eds.). (2009). Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration. IGI Global. https://doi.org/10.4018/978-1-60566-274-9

Chicago

Prince, Violaine , and Mathieu Roche, eds. Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-274-9

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Today, there is an intense interest for bio natural language processing (NLP) creating a need among researchers, academicians, and practitioners for a comprehensive publication of articles in this area.

Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration provides relevant theoretical frameworks and the latest empirical research findings in this area according to a linguistic granularity. As a critical mass of advanced knowledge, this book presents original applications, going beyond existing publications while opening up the road for a broader use of NLP in biomedicine.

Table of Contents

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Front Materials

Title Page

Copyright Page

Preface

Chapters

Chapter 1

Text Mining for Biomedicine (pages 1-9)

Sophia Ananiadou

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...

Works at a Lexical Level

Chapter 2

Lexical Granularity for Automatic Indexing and Means to Achieve It: The Case of Swedish MeSH® (pages 11-37)

Dimitrios Kokkinakis

The identification and mapping of terminology from large repositories of life science data onto concept hierarchies constitute an important initial step for a deeper semantic exploration of unstructured textual content. Accurate and...

Chapter 3

Expanding Terms with Medical Ontologies to Improve a Multi-Label Text Categorization System (pages 38-57)

M. Teresa Martín-Valdivia, Arturo Montejo-Ráez, M. C. Díaz-Galiano, José M. Perea Ortega, L. Alfonso Ureña-López

This chapter argues for the integration of clinical knowledge extracted from medical ontologies in order to improve a Multi-Label Text Categorization (MLTC) system for medical domain. The approach is based on the concept of semantic...

Chapter 4

Using Biomedical Terminological Resources for Information Retrieval (pages 58-77)

Piotr Pezik, Antonio Jimeno Yepes, Dietrich Rebholz-Schuhmann

The present chapter discusses the use of terminological resources for Information Retrieval in the biomedical domain. The authors first introduce a number of example resources which can be used to compile terminologies for biomedical...

Chapter 5

Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval (pages 78-105)

Laura Diosan, Alexandrina Rogozan, Jean-Pierre Pécuchet

The automatic alignment between a specialized terminology used by librarians in order to index concepts and a general vocabulary employed by a neophyte user in order to retrieve medical information will certainly improve the...

Chapter 6

Translation of Biomedical Terms by Inferring Rewriting Rules (pages 106-123)

Vincent Claveau

This chapter presents a simple yet efficient approach to translate automatically unknown biomedical terms from one language into another. This approach relies on a machine learning process able to infer rewriting rules from examples...

Chapter 7

Lexical Enrichment of Biomedical Ontologies (pages 124-141)

Nils Reiter, Paul Buitelaar

This chapter is concerned with lexical enrichment of ontologies, that is how to enrich a given ontology with lexical information derived from a semantic lexicon such as WordNet or other lexical resources. The authors present an...

Chapter 8

Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach (pages 142-161)

Torsten Schiemann, Ulf Leser, Jörg Hakenberg

Ambiguity is a common phenomenon in text, especially in the biomedical domain. For instance, it is frequently the case that a gene, a protein encoded by the gene, and a disease associated with the protein share the same name....

Going Beyond Words

Chapter 9

Information Extraction of Protein Phosphorylation from Biomedical Literature (pages 163-176)

M. Narayanaswamy, K. E. Ravikumar, Z. Z. Hu, K. Vijay-Shanker, C. H. Wu

Protein posttranslational modification (PTM) is a fundamental biological process, and currently few text mining systems focus on PTM information extraction. A rule-based text mining system, RLIMS-P (Rule-based LIterature Mining...

Chapter 10

CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence (pages 177-189)

Yves Kodratoff, Jérôme Azé, Lise Fontaine

This chapter argues that in order to extract significant knowledge from masses of technical texts, it is necessary to provide the field specialists with programming tools with which they themselves may use to program their text...

Chapter 11

Analyzing the Text of Clinical Literature for Question Answering (pages 190-220)

Yun Niu, Graeme Hirst

The task of question answering (QA) is to find an accurate and precise answer to a natural language question in some predefined text. Most existing QA systems handle fact-based questions that usually take named entities as the...

Pragmatics, Discourse Structures and Segment Level as the Last Stage in the NLP Offer to Biomedicine

Chapter 12

Discourse Processing for Text Mining (pages 222-254)

Nadine Lucas

This chapter presents the challenge of integrating knowledge at higher levels of discourse than the sentence, to avoid “missing the forest for the trees”. Characterisation tasks aimed at filtering collections are introduced, showing...

Chapter 13

A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems (pages 255-272)

Dimosthenis Kyriazis, Anastasios Doulamis, Theodora Varvarigou

In this chapter, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of information (medical content) retrieval systems. In greater detail, the user who searches for information is...

Chapter 14

Extracting Patient Case Profiles with Domain-Specific Semantic Categories (pages 273-287)

Yitao Zhang, Jon Patrick

The fast growing content of online articles of clinical case studies provides a useful source for extracting domain-specific knowledge for improving healthcare systems. However, current studies are more focused on the abstract of a...

NLP Software for IR in Biomedicine

Chapter 15

Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach (pages 289-300)

Laura I. Furlong, Ferran Sanz

SNPs constitute key elements in genetic epidemiology and pharmacogenomics. While data about genetic variation is found at sequence databases, functional and phenotypic information on consequences of the variations resides in...

Chapter 16

Verification of Uncurated Protein Annotations (pages 301-314)

Francisco M. Couto, Mário J. Silva, Vivian Lee, Emily Dimmer, Evelyn Camon, Rolf Apweiler

Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a large portion of the data contains a significant number of errors and therefore requires...

Chapter 17

A Software Tool for Biomedical Information Extraction (And Beyond) (pages 315-324)

Burr Settles

ABNER (A Biomedical Named Entity Recognizer) is an open-source software tool for text mining in the molecular biology literature. It processes unstructured biomedical documents in order to discover and annotate mentions of genes...

Chapter 18

Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering (pages 325-343)

Asanee Kawtrakul, Chaveevarn Pechsiri, Sachit Rajbhandari, Frederic Andres

Valuable knowledge has been distributed in heterogeneous formats on many different Web sites and other sources over the Internet. However, finding the needed information is a complex task since there is a lack of semantic relations...

Chapter 19

Seekbio: Retrieval of Spatial Relations for System Biology (pages 344-355)

Christophe Jouis, Magali Roux-Rouquié, Jean-Gabriel Ganascia

Identical molecules could play different roles depending of the relations they may have with different partners embedded in different processes, at different time and/or localization. To address such intricate networks that account...

Conclusion and Perspectives

Chapter 20

Analysing Clinical Notes for Translation Research: Back to the Future (pages 357-377)

Jon Patrick, Pooyan Asgari

There have been few studies of large corpora of narrative notes collected from the health clinicians working at the point of care. This chapter describes the principle issues in analysing a corpus of 44 million words of clinical...

Back Materials

Compilation of References

About the Contributors

Index

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