Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
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
A program for aligning sentences in bilingual corpora
Computational Linguistics - Special issue on using large corpora: I
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
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
New techniques for context modeling
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Local context selection for aligning sentences in parallel corpora
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Robust utilization of context in word sense disambiguation
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
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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This paper presents a language-independent context-based sentence alignment technique given parallel corpora. We can view the problem of aligning sentences as finding translations of sentences chosen from different sources. Unlike current approaches which rely on pre-defined features and models, our algorithm employs features derived from the distributional properties of words and does not use any language dependent knowledge. We make use of the context of sentences and the notion of Zipfian word vectors which effectively models the distributional properties of words in a given sentence. We accept the context to be the frame in which the reasoning about sentence alignment is done. We evaluate the performance of our system based on two different measures: sentence alignment accuracy and sentence alignment coverage. We compare the performance of our system with commonly used sentence alignment systems and show that our system performs 1.2149 to 1.6022 times better in reducing the error rate in alignment accuracy and coverage for moderately sized corpora.