A Software Tool for Biomedical Information Extraction (And Beyond)

A Software Tool for Biomedical Information Extraction (And Beyond)

Burr Settles (University of Wisconsin-Madison, USA)
DOI: 10.4018/978-1-60566-274-9.ch017
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Abstract

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, proteins, cell types, and other entities of interest. This task, known as named entity recognition (NER), is an important first step for many larger information management goals in biomedicine, namely extraction of biochemical relationships, document classification, information retrieval, and the like. To accomplish this task, ABNER uses state-of-the-art machine learning models for sequence labeling called conditional random fields (CRFs). The software distribution comes bundled with two models that are pre-trained on standard evaluation corpora. ABNER can run as a stand-alone application with a graphical user interface, or be accessed as a Java API allowing it to be re-trained with new labeled corpora and incorporated into other, higher-level applications. This chapter describes the software and its features, presents an overview of the underlying technology, and provides a discussion of some of the more advanced natural language processing systems for which ABNER has been used as a component. ABNER is open-source and freely available from http://pages. cs.wisc.edu/~bsettles/abner/
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Introduction

Efforts to organize the wealth of biomedical knowledge in the primary literature have resulted in hundreds of databases and other resources (Bateman, 2008), providing scientists with access to structured biological information. However, with nearly half a million new research articles added to PubMed annually (Soteriades & Falagas, 2005), the sheer volume of publications and complexity of the knowledge to be extracted is beyond the means of most manual database curation efforts. As a result, many of these resources struggle to remain current. Automated information extraction (IE), or at least automated assistance for such extraction tasks, seems a natural way to overcome these information management bottlenecks.

Named entity recognition (NER) is a subtask of IE, focused on finding mentions of various entities that belong to semantic classes of interest. In the biomedical domain, entities of interest are usually references to genes, proteins, cell types, and the like. Accurate NER systems are an important first step for many larger information management goals, such as automatic extraction of biologically relevant relationships (e.g., protein-protein interactions or sub-cellular location of gene products), biomedical document classification and retrieval, and ultimately the automatic maintenance of biomedical databases.

In order to facilitate and encourage research in the area of biomedical NER, several “bake-off” style competitions have been organized, in particular the NLPBA shared task (Kim et al., 2004) and the BioCreative challenge (Yeh et al., 2005). For these events, several research teams rapidly design, build, and submit results for machine learning systems using benchmark annotated text collections. The challenges showcase a variety of approaches to the problem, and provide a wealth of insights into what sorts of models and features are most effective. However, few of the resulting systems have been made publicly available for researchers working in related areas of natural language processing (NLP) in biomedicine.

I first released ABNER (Settles, 2005) in July 2004 as a demonstrational graphical user interface (GUI) for the system I developed as part of the NLPBA shared task challenge (Settles, 2004). In March 2005, a revised, open-source version of the software was released with some performance improvements and a new Java application programming interface (API). The goal is to encourage others to write custom interfaces to the core NER software, allowing it to be integrated into other, more sophisticated biomedical information management systems. ABNER also supports training new models on corpora labeled for different knowledge domains (e.g., particular organisms, since gene naming conventions vary from species to species).

Figure 1 shows a screenshot of the intuitive GUI when ABNER is run as a stand-alone application. Text can be typed in manually or loaded from a file (top window), and then automatically tagged for multiple entities in real time (bottom window). Each entity type is highlighted with a unique color for easy visual reference, and tagged documents can be saved in a variety of annotated file formats. The application also has options for processing plain text documents on the file system in batch mode offline.

Figure 1.

A screenshot of ABNER’s graphical user interface

ABNER has built-in functionality for tokenization and sentence segmentation, which are fairly robust to line breaks and biomedical abbreviations (users can choose to bypass these features in favor of their own text preprocessing as well). The bundled ABNER application is implemented in Java and is therefore platform-independent, and has been tested on Linux, Solaris, Mac OS X, and Windows.

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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
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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