CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence

CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence

Yves Kodratoff (University Paris-Sud (Paris XI), France), Jérôme Azé (University Paris-Sud (Paris XI), France) and Lise Fontaine (Cardiff University, UK)
DOI: 10.4018/978-1-60566-274-9.ch010
OnDemand PDF Download:
$37.50

Abstract

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 analysis tools. These programming tools, besides helping the programming effort of the field specialists, must also help them to gather the field knowledge necessary for defining and retrieving what they define as significant knowledge. This necessary field knowledge must be included in a well-structured and easy to use part of the programming tool. In this chapter, we present CorTag, a programming tool which is designed to correct existing tags in a text and to assist the field specialist to retrieve the knowledge and/or information he or she is looking for.
Chapter Preview
Top

Introduction And Motivation

In this paper we present a new programming language, called CorTag, which is devoted to tagging words within the boundaries of the sentence in which they are contained. The context we are concerned with here is therefore limited to the sentence and the words within it. The tagging process in CorTag includes syntactic, functional and semantic tags. Ultimately CorTag is designed to correct the existing tags in highly specialised or technical texts.

Our primary aim is to contribute to the creation of a system which is able to find interesting pieces of knowledge within specialised texts. There is no attempt being made towards the broader understanding of natural language. Our ambition is to be able to spot parts of the texts that may be of particular interest to the specialist of a given technical domain. As we shall see, the process does nevertheless require a kind of ‘primitive’ understanding of the text.

In creating this new language, we have been motivated by two facts which, despite being intuitively obvious, are challenging when used as a base for the building of a computer system.

The first of these is that the number of genre specific texts is increasing exponentially. It follows that humans can no longer handle these masses of texts and the whole process has to be automated. The scientific community is certainly aware of this need as it is exemplified by the large number of competitions and challenges, dealing with many topics expressed in many different languages. This has led to the development of software solutions devoted to solving at least one of the problems encountered for each step of the overall process. In order to make these steps explicit, let us propose a tentative list of the main steps involved. The text mining process starts by gathering the texts of interest, what we will refer to as ‘text gathering’. The process ends when the desired information has been found in the text. This final step is identified here as ‘information extraction’. There is a large set of intermediate steps which take place between these two steps, and the precise set of steps depends on the state of the retrieved texts and the nature of the information sought. The following sequence shows one possible ordering of the necessary intermediate steps:

text gathering sorting standardization creation/improvement of lexicon tagging and/or parsing terminology concept recognition co-reference resolution finding the relations among concepts information extraction.

In the following, when speaking of any step in particular, we will always assume that all n-k steps have been executed before the current stepn. We shall not, however, assume that they have been correctly completed. One of the main difficulties is that these different levels of Natural Language (NL) processing are mutually dependent. In general, the context independent processes can be performed quite satisfactorily, while the context dependent ones are very challenging as we shall exemplify. Unfortunately, the users (and sometimes even the creators) of the ‘stepn specialized software’ are not aware that this software is absolutely unable to function properly if some of stepn-k has not been properly completed. For example, ‘sorting’, a step which will be described later in the paper, illustrates well the dependencies amongst steps. Sorting is not really context-dependent, as we shall explain, and therefore it is a step which should be completed relatively easily. However, an improperly performed stepn causes mistakes at stepn+k which then spread throughout the process. It is the context dependent steps which are most greatly affected by this. Since many of the context dependent mistakes of stepn-k cannot be detected before stepn, we need a language to backtrack and correct them. This defines the first primary constraint placed on CorTag’s development.

Complete Chapter List

Search this Book:
Reset
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®
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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
$37.50
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)
$37.50
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
$37.50
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
$37.50
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
$37.50
About the Contributors