Cross-Lingual Text Categorization
Lecture notes in computer sciencePublished 1 January 2003
Núria Bel, C. H. A. Koster, Marta Villegas
Citations143
SJR quartileQ2
SJR score0.35
SNIP0.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Practical and cost-effective solutions for automatic Cross-Lingual Text Categorization are described, both in case a sufficient number of training examples is available for each new language and in the case that for some language no training examples are available.
Abstract
This article deals with the problem of Cross-Lingual Text Categorization (CLTC), which arises when documents in different languages must be classified according to the same classification tree. We describe practical and cost-effective solutions for automatic Cross-Lingual Text Categorization, both in case a sufficient number of training examples is available for each new language and in the case that for some language no training examples are available. Experimental results of the bi-lingual classification of the ILO corpus (with documents in English and Spanish) are obtained using bi-lingual training, terminology translation and profile-based translation.
Keywords
Computer Science
American Journal of PhysicsHandbook of Mathematical Functions
40,436 Citations1966Donald A. McQuarrie
ACM Computing SurveysMachine learning in automated text categorization
7,899 Citations2002Fabrizio Sebastiani
This survey discusses the main approaches to text categorization that fall within the machine learning paradigm and discusses in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
ACM SIGIR ForumInformation Retrieval as Statistical Translation
631 Citations2017Adam Berger, John Lafferty
A simple, well motivated model of the document-to-query translation process is proposed, and an algorithm for learning the parameters of this model in an unsupervised manner from a collection of documents is described.
An evaluation of phrasal and clustered representations on a text categorization task
547 Citations1992David Lewis
It is shown that optimal effectiveness occurs when using only a small proportion of the indexing terms available, and that effectiveness peaks at a higher feature set size and lower effectiveness level for a syntactic phrase indexing than for word-based indexing.
Information retrieval as statistical translation
390 Citations1999Adam Berger, John Lafferty
A simple, well motivated model of the document-to-query translation process is proposed, and an algorithm for learning the parameters of this model in an unsupervised manner from a collection of documents is described.
Cross-lingual relevance models
217 Citations2002Victor Lavrenko, Martin Choquette +1 more
A formal model of Cross-Language Information Retrieval that does not rely on either query translation or document translation and integrates popular techniques of disambiguation and query expansion in a unified formal framework is proposed.
Natural language processingAutomatic term detection
208 Citations2001Maria Teresa Cabré i Castellví, Rosa Estopà +1 more
A patent search and classification system
201 Citations1999Leah S. Larkey
A system for searching and classifying U.S. patent documents, based on Inquery, which includes a unique “phrase help” facility, which helps users find and add phrases and terms related to those in their query.
Natural language processingRecent Advances in Computational Terminology
183 Citations2001
A graph-based approach to the automatic generation of multilingual keyword clusters and a conceptual and methodological framework for Extracting knowledge-rich contexts for terminography.
A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization
180 Citations2001Maria Fernanda Caropreso, Stan Matwin +1 more
This work investigates the usefulness of n-grams for document indexing in text categorization (TC) independently of any specific learning algorithm, by applying feature selection to the pool of all k- grams, and checking how many n- Grams score high enough to be selected in the top σ k-rams.
arXiv (Cornell University)Mistake-Driven Learning in Text Categorization
144 Citations1997Ido Dagan, Yael Karov +1 more
This work studies three mistake-driven learning algorithms for a typical task of this nature -- text categorization and presents an algorithm, a variation of Littlestone's Winnow, which performs significantly better than any other algorithm tested on this task using a similar feature set.
Machine LearningGeneral Convergence Results for Linear Discriminant Updates
124 Citations2001Adam J. Grove, Nick Littlestone +1 more
A single proof of convergence is given that covers a broad subset of algorithms in this class of “quasi-additive” algorithms, including both Perceptron and Winnow, but also many new algorithms, and demonstrates that the unifying principles are more broadly applicable.
Little words can make a big difference for text classification
117 Citations1995Ellen Riloff
This work presents results from text classification experiments that compare relevancy signatures, which use local linguistic context, with corresponding indexing terms that do not, and suggests that stopword lists and stemming algorithms may remove or conflate many words that could be used to create more effective indexing Terms.
Comparing cross-language query expansion techniques by degrading translation resources
117 Citations2002Paul McNamee, James Mayfield
In several instances, pre- translation expansion results in better performance when no translations are available, than when an uncompromised resource is used without pre-translation expansion, and it is demonstrated that post-translation Expansion using relevance feedback can confer modest performance gains.
Lecture notes in computer scienceDisambiguation Strategies for Cross-Language Information Retrieval
70 Citations1999Djoerd Hiemstra, Franciska de Jong
This paper gives an overview of tools and methods for Cross-Language Information Retrieval (CLIR) that are developed within the Twenty-One project with the TREC CLIR task document collection using Dutch queries on the English document base to evaluate two approaches to disambiguation.
Improved cross-language retrieval using backoff translation
56 Citations2001Philip Resnik, Douglas W. Oard +1 more
Two techniques for combining evidence from dictionary-based and corpus-based translation lexicons are presented, and it is shown that backoff translation outperforms a technique based on merging lexicons.
Lecture notes in computer scienceTaming Wild Phrases
34 Citations2003C. H. A. Koster, M. Seutter
It is concluded that even the most careful term selection cannot overcome the differences in Document Frequency between phrases and words, and the use of term clustering to make phrases more cooperative is proposed.
Text Databases and Document Management: Theory and Practice
34 Citations2000Amita Goyal Chin
A discussion of concrete theories and practices in the handling of textual data as well as documents as a whole about search engine efficacy on the World Wide Web, metadata for ad hoc documents, text database theory and practice, and issues in document exchange.
Improved Cross-Language Retrieval using Backoff Translation
15 Citations2001Philip Resnik, Douglas W. Oard +1 more
Lecture notes in computer scienceUncertainty-Based Noise Reduction and Term Selection in Text Categorization
12 Citations2002Charles M. Peters, C. H. A. Koster
Uncertainty-based term selection is compared to a number of other criteria like Information Gain, simplified ?