Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Translingual vocabulary mappings for multilingual information access
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Fast and Accurate Sentence Alignment of Bilingual Corpora
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
MSR-MT: The Microsoft Research Machine Translation System
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
Computational Linguistics - Special issue on web as corpus
Embedding web-based statistical translation models in cross-language information retrieval
Computational Linguistics - Special issue on web as corpus
A program for aligning sentences in bilingual corpora
Computational Linguistics - Special issue on using large corpora: I
A class-based approach to word alignment
Computational Linguistics
Bitext maps and alignment via pattern recognition
Computational Linguistics
A portable algorithm for mapping bitext correspondence
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Aligning sentences in parallel corpora
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Aligning sentences in bilingual corpora using lexical information
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
A part-of-speech-based alignment algorithm
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Building parallel corpora by automatic title alignment using length-based and text-based approaches
Information Processing and Management: an International Journal
An approach based on multilingual thesauri and model combination for bilingual lexicon extraction
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Stemming to improve translation lexicon creation form bitexts
Information Processing and Management: an International Journal
Improved unsupervised sentence alignment for symmetrical and asymmetrical parallel corpora
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Extracting parallel paragraphs and sentences from english-persian translated documents
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Parallel corpora have become an essential resource for work in multilingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross-language information retrieval and machine translation applications. In this paper, we present two new approaches to align English-Arabic sentences in bilingual parallel corpora based on probabilistic neural network (P-NNT) and Gaussian mixture model (GMM) classifiers. A feature vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the probabilistic neural network and Gaussian mixture model. Another set of data was used for testing. Using the probabilistic neural network and Gaussian mixture model approaches, we could achieve error reduction of 27% and 50%, respectively, over the length based approach when applied on a set of parallel English-Arabic documents. In addition, the results of (P-NNT) and (GMM) outperform the results of the combined model which exploits length, punctuation and cognates in a dynamic framework. The GMM approach outperforms Melamed and Moore's approaches too. Moreover these new approaches are valid for any languages pair and are quite flexible since the feature vector may contain more, less or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.