Identifying correspondences between words

Ontologies are useful tools for sharing and exchanging knowledge. However ontology construction is complex and often time consuming. In this paper, we present a method for building a bilingual domain ontology from textual and termino-ontological resources intended for semantic annotation and information retrieval of textual documents. This method combines two approaches: ontology learning from texts and the reuse of existing terminological resources. It consists of four steps: (i) term extraction from domain specific corpora (in French and English) using textual analysis tools, (ii) clustering of terms into concepts organized according to the UMLS Metathesaurus, (iii) ontology enrichment through the alignment of French and English terms using parallel corpora and the integration of new concepts, (iv) refinement and validation of results by domain experts. These validated results are formalized into a domain ontology dedicated to Alzheimer's disease and related syndromes which is available online (http://lesim.isped.u-bordeaux2.fr/SemBiP/ressources/ontoAD.owl). The latter currently includes 5765 concepts linked by 7499 taxonomic relationships and 10,889 non-taxonomic relationships. Among these results, 439 concepts absent from the UMLS were created and 608 new synonymous French terms were added. The proposed method is sufficiently flexible to be applied to other domains.

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Projecting POS tags and syntactic dependencies from English and French to Polish in aligned corpora

Ozdowska

2006

Proceedings of the International Workshop on Cross-Language Knowledge Induction - CrossLangInduction '06

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This paper presents the first step to project POS tags and dependencies from English and French to Polish in aligned corpora. Both the English and French parts of the corpus are analysed with a POS tagger and a robust parser. The English/Polish bi-text and the French/Polish bi-text are then aligned at the word level with the GIZA++ package. The intersection of IBM-4 Viterbi alignments for both translation directions is used to project the annotations from English and French to Polish. The results show that the precision of direct projection vary according to the type of induced annotations as well as the source language. Moreover, the performances are likely to be improved by defining regular conversion rules among POS tags and dependencies.

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Identifying correspondences between words

Cited by 5 publications

References 14 publications

Multi-task Learning for Word Alignment and Dependency Parsing

Multi-task Learning for Word Alignment and Dependency Parsing

Reuse of termino-ontological resources and text corpora for building a multilingual domain ontology: An application to Alzheimer’s disease

Projecting POS tags and syntactic dependencies from English and French to Polish in aligned corpora

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