Lexical Granularity for Automatic Indexing and Means to Achieve It: The Case of Swedish MeSH®

Lexical Granularity for Automatic Indexing and Means to Achieve It: The Case of Swedish MeSH®

Dimitrios Kokkinakis (University of Gothenburg, Sweden)
DOI: 10.4018/978-1-60566-274-9.ch002
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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 efficient mapping of this kind is likely to provide better means of enhancing indexing and retrieval of text, uncovering subtle differences, similarities and useful patterns, and hopefully new knowledge, among complex surface realisations, overlooked by shallow techniques based on various forms of lexicon look-up approaches. However, a finer-grained level of mapping between terms as they occur in natural language and domain concepts is a cumbersome enterprise that requires various levels of processing in order to make explicit relevant linguistic structures. This chapter highlights some of the challenges encountered in the process of bridging free text to controlled vocabularies and thesauri and vice versa. The author investigates how the extensive variability of lexical terms in authentic data can be efficiently projected to hierarchically structured codes, while means to increase the coverage of the underlying lexical resources are also investigated.
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Large repositories of life science data in the form of domain-specific literature, textual databases and other large specialised textual collections (corpora) in electronic form increase on a daily basis to a level beyond what the human mind can grasp and interpret. As the volume of data continues to increase, substantial support from new information technologies and computational techniques grounded in the form of the ever increasing applications of the mining paradigm is becoming apparent. In the biomedical domain, for instance, curators are struggling to effectively process tens of thousands of scientific references that are added monthly to the MEDLINE/PubMed database. While, in the clinical setting vast amounts of health-related data are collected on a daily basis. They constitute a valuable research resource particularly if they by effective automated processing could be better integrated and linked, and thus help scientists to locate and make better use of the knowledge encoded in the electronic repositories. One example would be the construction of hypotheses based upon associations between extracted information possibly overlooked by human readers. Web, Text and Data mining are therefore recognised as the key technologies for advanced, exploratory and quantitative data-analysis of large and often complex data in unstructured or semi-structured form in document collections. Text mining is the technology that tries to solve the problem of information overload by combining techniques from natural language processing (NLP), information retrieval, machine learning, visualization and knowledge management, by the analysis of large volumes of unstructured data and the development of new tools and/or integration/adaptation of state of the art processing components. “Text mining aims at extracting interesting non-trivial patterns of knowledge by discovering, extracting and linking sparse evidence from various sources” (Hearst, 1999) and is considered a variation of data mining, which tries to find interesting patterns in structured data, while in the same analogy, web mining is the analysis of useful information directly from web documents (Markellos et al., 2004). These emerging technologies play an increasingly critical role in aiding research productivity, and they provide the means for reducing the workload for information access and decision support and for speeding up and enhancing the knowledge discovery process (Kao & Poteet, 2007; Feldman& Sanger, 2007; Sirmakessis, 2004).

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Table of Contents
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
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®
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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
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
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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