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Quantifying Cross-lingual Semantic Similarity for Natural Language Processing Applications ...
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Quantifying Cross-lingual Semantic Similarity for Natural Language Processing Applications
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Abstract:
Translation and cross-lingual access to information are key technologies in a global economy. Even though the quality of machine translation (MT) output is still far from the level of human translations, many real-world applications have emerged, for which MT can be employed. Machine translation supports human translators in computer-assisted translation (CAT), providing the opportunity to improve translation systems based on human interaction and feedback. Besides, many tasks that involve natural language processing operate in a cross-lingual setting, where there is no need for perfectly fluent translations and the transfer of meaning can be modeled by employing MT technology. This thesis describes cumulative work in the field of cross-lingual natural language processing in a user-oriented setting. A common denominator of the presented approaches is their anchoring in an alignment between texts in two different languages to quantify the similarity of their content.
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Keyword:
004; 004 Data processing Computer science; 400; 400 Linguistics
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URL: https://nbn-resolving.org/urn:nbn:de:bsz:16-heidok-190461
https://archiv.ub.uni-heidelberg.de/volltextserver/19046/
https://doi.org/10.11588/heidok.00019046
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