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- item-1 hasContent lori-lamel.
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- 512 creator kyo-kageura.
- 512 creator takeshi-abekawa.
- 512 type InProceedings.
- 512 label "Constructing a Corpus that Indicates Patterns of Modification between Draft and Final Translations by Human Translators".
- 512 sameAs 512.
- 512 abstract "In human translation, translators first make draft translations and then modify and edit them. In the case of experienced translators, this process involves the use of wide-ranging expert knowledge, which has mostly remained implicit so far. Describing the difference between draft and final translations, therefore, should contribute to making this knowledge explicit. If we could clarify the expert knowledge of translators, hopefully in a computationally tractable way, we would be able to contribute to the automatic notification of awkward translations to assist inexperienced translators, improving the quality of MT output, etc. Against this backdrop, we have started constructing a corpus that indicates patterns of modification between draft and final translations made by human translators. This paper reports on our progress to date.".
- 512 hasAuthorList authorList.
- 512 hasTopic Linguistics.
- 512 isPartOf proceedings.
- 512 keyword "Corpus (creation, annotation, etc.)".
- 512 keyword "Machine Translation, SpeechToSpeech Translation".
- 512 keyword "Multilinguality".
- 512 title "Constructing a Corpus that Indicates Patterns of Modification between Draft and Final Translations by Human Translators".
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- 514 creator mehdi-embarek.
- 514 creator olivier-ferret.
- 514 type InProceedings.
- 514 label "Learning Patterns for Building Resources about Semantic Relations in the Medical Domain".
- 514 sameAs 514.
- 514 abstract "In this article, we present a method for extracting automatically semantic relations from texts in the medical domain using linguistic patterns. These patterns refer to three levels of information about words: inflected form, lemma and part-of-speech. The method we present consists first in identifying the entities that are part of the relations to extract, that is to say diseases, exams, treatments, drugs or symptoms. Thereafter, sentences that contain couples of entities are extracted and the presence of a semantic relation is validated by applying linguistic patterns. These patterns were previously learnt automatically from a manually annotated corpus by relying onan algorithm based on the edit distance. We first report the results of an evaluation of our medical entity tagger for the five types of entities we have mentioned above and then, more globally, the results of an evaluation of our extraction method for four relations between these entities. Both evaluations were done for French.".
- 514 hasAuthorList authorList.
- 514 hasTopic Linguistics.
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- 514 keyword "Acquisition, Machine Learning".
- 514 keyword "Ontologies".
- 514 keyword "Semantics".
- 514 title "Learning Patterns for Building Resources about Semantic Relations in the Medical Domain".
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- 515 creator john-mcnaught.
- 515 creator scott-piao.
- 515 creator sophia-ananiadou.
- 515 type InProceedings.
- 515 label "Clustering Related Terms with Definitions".
- 515 sameAs 515.
- 515 abstract "It is a challenging task to match similar or related terms/expressions in NLP and Text Mining applications. Two typical areas in need for such work are terminology and ontology constructions, where terms and concepts are extracted and organized into certain structures with various semantic relations. In the EU BOOTSTrep Project we test various techniques for matching terms that can assist human domain experts in building and enriching ontologies. This paper reports on a work in which we evaluated a text comparing and clustering tool for this task. Particularly, we explore the feasibility of matching related terms with their definitions. Ontology terms, such as Gene Ontology terms, are often assigned with detailed definitions, which provide a fundamental information source for detecting relations between terms. Here we focus on the exploitation of term definitions for the term matching task. Our experiment shows that the tool is capable of grouping many related terms using their definitions.".
- 515 hasAuthorList authorList.
- 515 hasTopic Linguistics.
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- 515 keyword "Ontologies".
- 515 keyword "Text mining".
- 515 keyword "Tools, systems, applications".
- 515 title "Clustering Related Terms with Definitions".
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