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Multilingual Text Classification using Ontologies

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Gerard de Melo
Gerard de Melo

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.

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Introduction Techniques Overview and Summary Multilingual Text Classification Gerard de Melo, Stefan Siersdorfer Max Planck Institute for Computer Science Saarbr¨ucken, Germany 2007-04-04 G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Text Classification Text Classification task: automatically assign text documents to classes (e.g. thematically, geographically) machine learning algorithms, e.g. SVM, can learn from pre-classified training documents multilingual case: documents in multiple languages applications: news wire filtering, library management, e-mail, etc. G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Text Classification Text Classification task: automatically assign text documents to classes (e.g. thematically, geographically) machine learning algorithms, e.g. SVM, can learn from pre-classified training documents multilingual case: documents in multiple languages applications: news wire filtering, library management, e-mail, etc. G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Text Classification Text Classification task: automatically assign text documents to classes (e.g. thematically, geographically) machine learning algorithms, e.g. SVM, can learn from pre-classified training documents multilingual case: documents in multiple languages applications: news wire filtering, library management, e-mail, etc. G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Text Classification Text Classification task: automatically assign text documents to classes (e.g. thematically, geographically) machine learning algorithms, e.g. SVM, can learn from pre-classified training documents multilingual case: documents in multiple languages applications: news wire filtering, library management, e-mail, etc. G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight 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 languages lexical ambiguity in LI (unnecessary introduction of additional ambiguity) G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight 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 languages lexical ambiguity in LI (unnecessary introduction of additional ambiguity) G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight 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 languages lexical ambiguity in LI (unnecessary introduction of additional ambiguity) Spanish coche −→ car French voiture −→ automobile G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Semantic Concepts Idea map all words to semantic concepts (e.g. WordNet synsets), thus distinguishing different senses of a word while identifying synonyms disambiguate using context information construct feature vectors by counting occurrences of concepts rather than terms G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Semantic Concepts Idea map all words to semantic concepts (e.g. WordNet synsets), thus distinguishing different senses of a word while identifying synonyms disambiguate using context information construct feature vectors by counting occurrences of concepts rather than terms G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Semantic Concepts Idea map all words to semantic concepts (e.g. WordNet synsets), thus distinguishing different senses of a word while identifying synonyms disambiguate using context information construct feature vectors by counting occurrences of concepts rather than terms G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight 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 t^ete *I have pain at the head G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Weight Propagation propagate weight from original concepts to related concepts choose path to c maximizing its weight Dijkstra-like algorithm in order to assign maximal possible weight to a concept G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Weight Propagation propagate weight from original concepts to related concepts choose path to c maximizing its weight Dijkstra-like algorithm in order to assign maximal possible weight to a concept G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Weight Propagation propagate weight from original concepts to related concepts choose path to c maximizing its weight Dijkstra-like algorithm in order to assign maximal possible weight to a concept G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
Introduction Techniques Overview and Summary Overview and Summary Overview and Summary Ontology Region Mapping 1 optionally translate the documents – or use a multilingual lexical 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
Introduction Techniques Overview and Summary Overview and Summary Overview and Summary Ontology Region Mapping 1 optionally translate the documents – or use a multilingual lexical 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
Introduction Techniques Overview and Summary Overview and Summary Overview and Summary Ontology Region Mapping 1 optionally translate the documents – or use a multilingual lexical resource (aligned wordnets) 2 map terms to concepts 3 search for highly related concepts entire regions of concepts are relevant, so propagate a part of the concept’s weight to related concepts G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification

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Multilingual Text Classification using Ontologies

  • 1. Introduction Techniques Overview and Summary Multilingual Text Classification Gerard de Melo, Stefan Siersdorfer Max Planck Institute for Computer Science Saarbr¨ucken, Germany 2007-04-04 G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 2. Introduction Techniques Overview and Summary Text Classification Text Classification task: automatically assign text documents to classes (e.g. thematically, geographically) machine learning algorithms, e.g. SVM, can learn from pre-classified training documents multilingual case: documents in multiple languages applications: news wire filtering, library management, e-mail, etc. G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 3. Introduction Techniques Overview and Summary Text Classification Text Classification task: automatically assign text documents to classes (e.g. thematically, geographically) machine learning algorithms, e.g. SVM, can learn from pre-classified training documents multilingual case: documents in multiple languages applications: news wire filtering, library management, e-mail, etc. G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 4. Introduction Techniques Overview and Summary Text Classification Text Classification task: automatically assign text documents to classes (e.g. thematically, geographically) machine learning algorithms, e.g. SVM, can learn from pre-classified training documents multilingual case: documents in multiple languages applications: news wire filtering, library management, e-mail, etc. G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 5. Introduction Techniques Overview and Summary Text Classification Text Classification task: automatically assign text documents to classes (e.g. thematically, geographically) machine learning algorithms, e.g. SVM, can learn from pre-classified training documents multilingual case: documents in multiple languages applications: news wire filtering, library management, e-mail, etc. G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 6. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight 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 languages lexical ambiguity in LI (unnecessary introduction of additional ambiguity) G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 7. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight 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 languages lexical ambiguity in LI (unnecessary introduction of additional ambiguity) G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 8. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight 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 languages lexical ambiguity in LI (unnecessary introduction of additional ambiguity) Spanish coche −→ car French voiture −→ automobile G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 9. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Semantic Concepts Idea map all words to semantic concepts (e.g. WordNet synsets), thus distinguishing different senses of a word while identifying synonyms disambiguate using context information construct feature vectors by counting occurrences of concepts rather than terms G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 10. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Semantic Concepts Idea map all words to semantic concepts (e.g. WordNet synsets), thus distinguishing different senses of a word while identifying synonyms disambiguate using context information construct feature vectors by counting occurrences of concepts rather than terms G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 11. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Semantic Concepts Idea map all words to semantic concepts (e.g. WordNet synsets), thus distinguishing different senses of a word while identifying synonyms disambiguate using context information construct feature vectors by counting occurrences of concepts rather than terms G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 12. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight 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 t^ete *I have pain at the head G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 13. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Weight Propagation propagate weight from original concepts to related concepts choose path to c maximizing its weight Dijkstra-like algorithm in order to assign maximal possible weight to a concept G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 14. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Weight Propagation propagate weight from original concepts to related concepts choose path to c maximizing its weight Dijkstra-like algorithm in order to assign maximal possible weight to a concept G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 15. Introduction Techniques Overview and Summary Machine Translation Mapping to Semantic Concept Weight Propagation Weight Propagation propagate weight from original concepts to related concepts choose path to c maximizing its weight Dijkstra-like algorithm in order to assign maximal possible weight to a concept G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification
  • 16. Introduction Techniques Overview and Summary Overview and Summary Overview and Summary Ontology Region Mapping 1 optionally translate the documents – or use a multilingual lexical 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
  • 17. Introduction Techniques Overview and Summary Overview and Summary Overview and Summary Ontology Region Mapping 1 optionally translate the documents – or use a multilingual lexical 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
  • 18. Introduction Techniques Overview and Summary Overview and Summary Overview and Summary Ontology Region Mapping 1 optionally translate the documents – or use a multilingual lexical resource (aligned wordnets) 2 map terms to concepts 3 search for highly related concepts entire regions of concepts are relevant, so propagate a part of the concept’s weight to related concepts G. de Melo, S. Siersdorfer, Max-Planck-Institut Informatik Multilingual Text Classification