multilingual text classification using ontologies
DESCRIPTION
In this paper, we investigate strategies for automatically classifying documents in different languages thematically, geographically or according to other criteria. A novel linguistically motivated text representation scheme is presented that can be used with machine learning algorithms in order to learn classifications from pre-classified examples and then automatically classify documents that might be provided in entirely different languages. Our approach makes use of ontologies and lexical resources but goes beyond a simple mapping from terms to concepts by fully exploiting the external knowledge manifested in such resources and mapping to entire regions of concepts. For this, a graph traversal algorithm is used to explore related concepts that might be relevant. Extensive testing has shown that our methods lead to significant improvements compared to existing approaches.TRANSCRIPT
IntroductionTechniques
Overview and Summary
Multilingual Text Classification
Gerard de Melo, Stefan Siersdorfer
Max Planck Institute for Computer ScienceSaarbrucken, Germany
2007-04-04
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and SummaryText Classification
Text Classification
task: automatically assign textdocuments to classes (e.g.thematically, geographically)
machine learning algorithms, e.g.SVM, can learn from pre-classifiedtraining documents
multilingual case: documents inmultiple languages
applications: news wire filtering,library management, e-mail, etc.
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and SummaryText Classification
Text Classification
task: automatically assign textdocuments to classes (e.g.thematically, geographically)
machine learning algorithms, e.g.SVM, can learn from pre-classifiedtraining documents
multilingual case: documents inmultiple languages
applications: news wire filtering,library management, e-mail, etc.
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and SummaryText Classification
Text Classification
task: automatically assign textdocuments to classes (e.g.thematically, geographically)
machine learning algorithms, e.g.SVM, can learn from pre-classifiedtraining documents
multilingual case: documents inmultiple languages
applications: news wire filtering,library management, e-mail, etc.
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and SummaryText Classification
Text Classification
task: automatically assign textdocuments to classes (e.g.thematically, geographically)
machine learning algorithms, e.g.SVM, can learn from pre-classifiedtraining documents
multilingual case: documents inmultiple languages
applications: news wire filtering,library management, e-mail, etc.
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Machine Translation for Multilingual TC
idea: simply translate all documents into a single language LI
(prior work by Jalam 2002, Rigutini et al. 2005)
shortcomings of this approach
lexical variety in LI (English: huge vocabulary, many synonyms)variety of expression in source languageslexical ambiguity in LI (unnecessary introduction of additionalambiguity)
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Machine Translation for Multilingual TC
idea: simply translate all documents into a single language LI
(prior work by Jalam 2002, Rigutini et al. 2005)
shortcomings of this approach
lexical variety in LI (English: huge vocabulary, many synonyms)variety of expression in source languageslexical ambiguity in LI (unnecessary introduction of additionalambiguity)
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Machine Translation for Multilingual TC
idea: simply translate all documents into a single language LI
(prior work by Jalam 2002, Rigutini et al. 2005)
shortcomings of this approach
lexical variety in LI (English: huge vocabulary, many synonyms)variety of expression in source languageslexical ambiguity in LI (unnecessary introduction of additionalambiguity)
Spanish coche 7−→ car
French voiture 7−→ automobile
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Semantic Concepts
Idea
map all words to semantic concepts (e.g. WordNet synsets),thus distinguishing different senses of a word while identifyingsynonyms
disambiguate using context information
construct feature vectors by counting occurrences of conceptsrather than terms
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Semantic Concepts
Idea
map all words to semantic concepts (e.g. WordNet synsets),thus distinguishing different senses of a word while identifyingsynonyms
disambiguate using context information
construct feature vectors by counting occurrences of conceptsrather than terms
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Semantic Concepts
Idea
map all words to semantic concepts (e.g. WordNet synsets),thus distinguishing different senses of a word while identifyingsynonyms
disambiguate using context information
construct feature vectors by counting occurrences of conceptsrather than terms
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Semantic Concepts
Problems
understemming
polysemy: highly related senses are treated as distinct
incongruent concepts between languages
variety of expression
lexical lacunae
English I have a headache I have a headache
Spanish Me duele la cabeza *It hurts the head to me
French J’ai mal a la tete *I have pain at the head
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Weight Propagation
propagate weight from originalconcepts to related concepts
choose path to c ′ maximizingits weight
Dijkstra-like algorithm in orderto assign maximal possibleweight to a concept
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Weight Propagation
propagate weight from originalconcepts to related concepts
choose path to c ′ maximizingits weight
Dijkstra-like algorithm in orderto assign maximal possibleweight to a concept
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and Summary
Machine TranslationMapping to Semantic ConceptWeight Propagation
Weight Propagation
propagate weight from originalconcepts to related concepts
choose path to c ′ maximizingits weight
Dijkstra-like algorithm in orderto assign maximal possibleweight to a concept
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and SummaryOverview and Summary
Overview and Summary
Ontology Region Mapping
1 optionally translate the documents – or use a multilinguallexical resource (aligned wordnets)
2 map terms to concepts
3 search for highly related concepts
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and SummaryOverview and Summary
Overview and Summary
Ontology Region Mapping
1 optionally translate the documents – or use a multilinguallexical resource (aligned wordnets)
2 map terms to concepts
3 search for highly related concepts
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
IntroductionTechniques
Overview and SummaryOverview and Summary
Overview and Summary
Ontology Region Mapping
1 optionally translate the documents – or use a multilinguallexical resource (aligned wordnets)
2 map terms to concepts
3 search for highly related concepts
entire regions of concepts arerelevant, so propagate a partof the concept’s weight torelated concepts
G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification