Using Comparable Corpora for Under-Resourced Areas of Machine Translation [electronic resource] /
Skadiņa, Inguna.editor.
Gaizauskas, Robert.editor.
Babych, Bogdan.editor.
Ljubešić, Nikola.editor.
Tufiş, Dan.editor.
Vasiļjevs, Andrejs.editor.
This book provides an overview of how comparable corpora can be used to overcome the lack of parallel resources when building machine translation systems for under-resourced languages and domains. It presents a wealth of methods and open tools for building comparable corpora from the Web, evaluating comparability and extracting parallel data that can be used for the machine translation task. It is divided into several sections, each covering a specific task such as building, processing, and using comparable corpora, focusing particularly on under-resourced language pairs and domains. The book is intended for anyone interested in data-driven machine translation for under-resourced languages and domains, especially for developers of machine translation systems, computational linguists and language workers. It offers a valuable resource for specialists and students in natural language processing, machine translation, corpus linguistics and computer-assisted translation, and promotes the broader use of comparable corpora in natural language processing and computational linguistics.
Introduction -- Cross-language comparability and its Applications for MT (Bogdan Babych, Fangzhong Su, Anthony Hartley, Ahmet Aker, Monica Lestari Paramita, Paul Clough, Robert Gaizauskas) -- Collecting comparable corpora (Monica Lestari Paramita, Ahmet Aker, Paul Clough, Robert Gaizauskas, Nikos Glaros, Nikos Mastropavlos, Olga Yannoutsou, Radu Ion, Dan Ștefănescu, Alexandru Ceauşu, Dan Tufiș and Judita Preiss) -- Extracting data from comparable corpora (Mārcis Pinnis, Nikola Ljubešić, Dan Ştefănescu, Inguna Skadiņa, Marko Tadić, Tatjana Gornostaja, Špela Vintar, Darja Fišer) -- Mapping and aligning units from comparable corpora (Ahmet Aker, Alexandru Ceaușu, Yang Feng, Robert Gaizauskas, Sabine Hunsicker, Radu Ion, Elena Irimia, Dan Ștefănescu, Dan Tufiș) -- Training, enhancing, evaluating and using MT-Systems with comparable data (Bogdan Babych, Yu Chen, Andreas Eisele, Sabine Hunsicker, Mārcis Pinnis, Inguna Skadiņa, Raivis Skadiņš, Gregor Thurmair, Andrejs Vasiļjevs, Mateja Verlic, Xiaojun Zhang) -- New areas of application of comparable corpora (Reinhard Rapp, Vivian Xu, Michael Zock, Serge Sharoff, Richard Forsyth, Bogdan Babych, Chenhui Chu, Toshiaki Nakazawa, Sadao Kurohashi) -- Appendices (Ahmet Aker, Radu Ion, Nikos Mastropavlos, Monica Paramita, Mārcis Pinnis, Dan Ştefănescu, Fangzhong Su, Gregor Thurmair,Elena Irimia, Nikola Ljubešić, Evangelos Kanoulas, Judita Preiss, Rob Gaizauskas, Paul Clough, Emma Barker, Nikos Glaros, Tiberiu Boroș, Inguna Skadiņa, Andrejs Vasiļjevs).
This book provides an overview of how comparable corpora can be used to overcome the lack of parallel resources when building machine translation systems for under-resourced languages and domains. It presents a wealth of methods and open tools for building comparable corpora from the Web, evaluating comparability and extracting parallel data that can be used for the machine translation task. It is divided into several sections, each covering a specific task such as building, processing, and using comparable corpora, focusing particularly on under-resourced language pairs and domains. The book is intended for anyone interested in data-driven machine translation for under-resourced languages and domains, especially for developers of machine translation systems, computational linguists and language workers. It offers a valuable resource for specialists and students in natural language processing, machine translation, corpus linguistics and computer-assisted translation, and promotes the broader use of comparable corpora in natural language processing and computational linguistics.
Natural language processing (Computer science).
Computational linguistics.
Data mining.
Natural Language Processing (NLP).
Computational Linguistics.
Data Mining and Knowledge Discovery.