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    • Pérez-Ortiz, Juan Antonio (2)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio; Sánchez-Martínez, Felipe. - : AI Access Foundation, 2016 BASE Show details 2 RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation Pérez-Ortiz, Juan Antonio; Sánchez-Cartagena, Víctor M.; Sánchez-Martínez, Felipe. - : De Gruyter, 2016 BASE Show details 3 RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation Sánchez-Cartagena Víctor M.; Pérez-Ortiz Juan Antonio; Sánchez-Martínez Felipe In: Prague Bulletin of Mathematical Linguistics , Vol 106, Iss 1, Pp 193-204 (2016) (2016) Abstract: This paper presents ruLearn, an open-source toolkit for the automatic inference of rules for shallow-transfer machine translation from scarce parallel corpora and morphological dictionaries. ruLearn will make rule-based machine translation a very appealing alternative for under-resourced language pairs because it avoids the need for human experts to handcraft transfer rules and requires, in contrast to statistical machine translation, a small amount of parallel corpora (a few hundred parallel sentences proved to be sufficient). The inference algorithm implemented by ruLearn has been recently published by the same authors in Computer Speech & Language (volume 32). It is able to produce rules whose translation quality is similar to that obtained by using hand-crafted rules. ruLearn generates rules that are ready for their use in the Apertium platform, although they can be easily adapted to other platforms. When the rules produced by ruLearn are used together with a hybridisation strategy for integrating linguistic resources from shallow-transfer rule-based machine translation into phrase-based statistical machine translation (published by the same authors in Journal of Artificial Intelligence Research, volume 55), they help to mitigate data sparseness. This paper also shows how to use ruLearn and describes its implementation. Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doaj.org/article/98d0d649c90f44769128720f137951c9 https://doi.org/10.1515/pralin-2016-0018 BASE Hide details

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