
Contents
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41.1 Introduction 41.1 Introduction
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41.1.1 What is a Term? 41.1.1 What is a Term?
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41.1.1.1 Relation with multiword expressions, collocations, and keyphrases 41.1.1.1 Relation with multiword expressions, collocations, and keyphrases
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41.2 Term Recognition 41.2 Term Recognition
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41.2.1 Linguistic Approaches 41.2.1 Linguistic Approaches
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41.2.2 Dictionary-Based Approaches 41.2.2 Dictionary-Based Approaches
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41.2.3 Statistical Approaches 41.2.3 Statistical Approaches
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41.2.3.1 Unithood-based approaches 41.2.3.1 Unithood-based approaches
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41.2.3.2 Termhood-based approaches 41.2.3.2 Termhood-based approaches
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41.2.4 Hybrid Approaches 41.2.4 Hybrid Approaches
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41.3 Term Recognition Critical Issues 41.3 Term Recognition Critical Issues
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41.3.1 Addressing Term Variability and Polysemy 41.3.1 Addressing Term Variability and Polysemy
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41.3.2 Domain Dependency and Reconfigurability 41.3.2 Domain Dependency and Reconfigurability
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41.3.3 Language Dependency 41.3.3 Language Dependency
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41.3.4 Scalability 41.3.4 Scalability
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41.4 Resources 41.4 Resources
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41.4.1 Evaluation 41.4.1 Evaluation
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41.4.1.1 Evaluation challenges 41.4.1.1 Evaluation challenges
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41.4.2 Ontologies 41.4.2 Ontologies
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41.4.3 Corpora 41.4.3 Corpora
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41.4.4 Automatic Term Recognition Systems 41.4.4 Automatic Term Recognition Systems
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41.5 Term Recognition Applications 41.5 Term Recognition Applications
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41.5.1 Document Classification and Clustering 41.5.1 Document Classification and Clustering
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41.5.2 Information Retrieval 41.5.2 Information Retrieval
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41.5.3 Automatic Summarization 41.5.3 Automatic Summarization
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41.5.4 Domain-Specific Lexicography and Ontology Building 41.5.4 Domain-Specific Lexicography and Ontology Building
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Further Reading and Relevant Resources Further Reading and Relevant Resources
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References References
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41 Term Extraction
Get accessIoannis Korkontzelos is a Reader in the Department of Computer Science, Edge Hill University, UK. His research focuses on Natural Language Processing and text mining. Particular research areas include compositional semantics, multiword expression, term extraction, document classification, and sentiment analysis, applied to domains such as open-source software, social sciences, social media, biomedicine, and scientific publications. Previously, Dr Korkontzelos was a research fellow at the National Centre for Text Mining, University of Manchester.
Sophia Ananiadou is Professor in Computer Science at the University of Manchester, Director of the National Centre for Text Mining, and a Turing Fellow. Since 2005, she has directed the National Centre for Text Mining (NaCTeM), carrying out world-leading research on text mining, which supports the provision of services, tools, resources, and infrastructure to a variety of users, from translational medicine to biology, biodiversity, humanities, and health and social sciences.
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Published:01 April 2014
Cite
Abstract
Automatic extraction of metadata from free text is key to digesting stored literature information, especially in dynamic and rapidly evolving fields, such as biomedicine. Besides, more and more applications heavily depend on knowledge and ontologies. Successfully recognizing or extracting terms and their relations in scientific and technical documents without human intervention is crucial to semantically structuring literature and populating ontologies. This task has been recognized as the bottleneck in exploiting fields that involve complex and dynamically changing terms, and thus has become an important research topic in Natural Language Processing. This chapter presents a brief but complete overview of automatic term recognition techniques and discusses a number of crucial practical issues. Subsequently, it focuses on evaluation, discusses available resources, and highlights a number of applications.
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