Text Mining for Biomedicine

Text Mining for Biomedicine

Sophia Ananiadou (University of Manchester, UK)
DOI: 10.4018/978-1-60566-274-9.ch001
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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.
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Introduction

Text mining covers a broad spectrum of activities and a battery of processes, but essentially the goal is to help users deal with information overload and information overlook (Ananiadou and McNaught, 2006). Key aspects are to discover unsuspected, new knowledge hidden in the vast scientific literature, to support data driven hypothesis discovery and to derive meaning from the rich language of specialists as expressed in the plethora of textual reports, articles, etc. With the overwhelming amount of information (~80%) in textual unstructured form and the growing number of publications, an estimate of about 2.5 million articles published per year (Harnad, Brody, Vallieres, Carr, Hitchcock, Gingras, Oppenheim, Stamerjohanns, and Hilf, 2004) it is not surprising that valuable new sources of research data typically remain underexploited and nuggets of insight or new knowledge are often never discovered in the sea of literature. Scientists are unable to keep abreast of developments in their fields and to make connections between seemingly unrelated facts to generate new ideas and hypotheses. Fortunately, text mining offers a solution to this problem by replacing or supplementing the human with automated means to turn unstructured text and implicit knowledge into structured data and thus explicit knowledge (Cohen, and Hunter, 2008; Hirschman, Park, Tsujii, and Wong, 2002; (McNaught and Black, 2006)(Jensen, Saric, and Bork, 2006; Hearst, 1999).

Text mining includes the following processes: information retrieval, information extraction and data mining.

Information Retrieval (IR) finds documents that answer an information need, with the aid of indexes. IR or ‘search engines’ such as Google™ and PubMed© typically classify a document as relevant or non relevant to a user’s query. To successfully find an item relevant to a search implies that this item has been sufficiently well characterised, indexed and classified such that relevance to a search query can be ascertained. Unfortunately, conventional information retrieval technology, while very good at handling large scale collections, remains at a rough granular level. Moreover, such technology typically focuses on finding sets of individual items, leaving it up to the user to somehow integrate and synthesise the knowledge contained in and across individual items. Thus, the content of documents is largely lost in conventional indexing approaches. To address this problem, we have improved the search strategy by placing more emphasis on terms in a collection of documents. In Biomedicine new terms are constantly created creating a severe obstacle to text mining and other natural language processing applications. In addition, term variation and ambiguity exacerbate the problem. We extract the most significant words in a collection of documents by using NaCTeM’s TerMine service.a TerMine extracts and automatically ranks technical terms based on our hybrid term extraction technique, C-value (Frantzi, Ananiadou, and Mima, 2000). The C-value scores are combined with the indexing capabilities of Lucene 2.2 for full text indexing and searching.

Based on the assumption that documents sharing similar words mention similar topics, the extracted terms can be used for subsequent associative search. The output of associative searching is a ranked list of documents similar to the original document. This allows us to link similar documents based on their content. Another enhancement of the search strategy is query expansion. One of the major criticisms with current search engines is that queries are effective only when well crafted. A desirable feature is automatic query expansion according to the users’ interests, but most search engines do not support this beyond mapping selective query terms to ontology headings (e.g. PubMedb). Therefore, there are inevitable limitations of coverage. To address this, we have used term-based automatic query expansion drawing upon weights given to terms discovered across different sized document sets. Query expansion embedded in searching allows the user to explore the wider collection, focusing on documents with similar significance and to discover potentially unknown documents

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Table of Contents
Preface
Violaine Prince, Mathieu Roche
Chapter 1
Sophia Ananiadou
Text mining provides the automated means to manage information overload and overlook. By adding meaning to text, text mining techniques produce a... Sample PDF
Text Mining for Biomedicine
$37.50
Chapter 2
Dimitrios Kokkinakis
The identification and mapping of terminology from large repositories of life science data onto concept hierarchies constitute an important initial... Sample PDF
Lexical Granularity for Automatic Indexing and Means to Achieve It: The Case of Swedish MeSH®
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Chapter 3
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... Sample PDF
Expanding Terms with Medical Ontologies to Improve a Multi-Label Text Categorization System
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Chapter 4
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... Sample PDF
Using Biomedical Terminological Resources for Information Retrieval
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Chapter 5
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... Sample PDF
Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval
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Chapter 6
Vincent Claveau
This chapter presents a simple yet efficient approach to translate automatically unknown biomedical terms from one language into another. This... Sample PDF
Translation of Biomedical Terms by Inferring Rewriting Rules
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Chapter 7
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... Sample PDF
Lexical Enrichment of Biomedical Ontologies
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Chapter 8
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... Sample PDF
Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach
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Chapter 9
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... Sample PDF
Information Extraction of Protein Phosphorylation from Biomedical Literature
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Chapter 10
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... Sample PDF
CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence
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Chapter 11
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... Sample PDF
Analyzing the Text of Clinical Literature for Question Answering
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Chapter 12
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... Sample PDF
Discourse Processing for Text Mining
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Chapter 13
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... Sample PDF
A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems
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Chapter 14
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... Sample PDF
Extracting Patient Case Profiles with Domain-Specific Semantic Categories
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Chapter 15
Laura I. Furlong, Ferran Sanz
SNPs constitute key elements in genetic epidemiology and pharmacogenomics. While data about genetic variation is found at sequence databases... Sample PDF
Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach
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Chapter 16
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... Sample PDF
Verification of Uncurated Protein Annotations
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Chapter 17
Burr Settles
ABNER (A Biomedical Named Entity Recognizer) is an open-source software tool for text mining in the molecular biology literature. It processes... Sample PDF
A Software Tool for Biomedical Information Extraction (And Beyond)
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Chapter 18
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... Sample PDF
Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering
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Chapter 19
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... Sample PDF
Seekbio: Retrieval of Spatial Relations for System Biology
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Chapter 20
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... Sample PDF
Analysing Clinical Notes for Translation Research: Back to the Future
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About the Contributors