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Improving unsupervised dependency parsing with richer contexts and smoothing
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Closed doors - Modelling intention in behavioural interfaces
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Using Hypernymy Acquisition to Tackle (Part of) Textual Entailment
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Multi-Document Summarisation Using Generic Relation Extraction
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Structured generative models for unsupervised named-entity clustering
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Coupling hierarchical word reordering and decoding in phrase-based statistical machine translation
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Improving grammaticality in statistical sentence generation : introducing a dependency spanning tree algorithm with an argument satisfaction model
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Ingesting the Auslan corpus into the DADA annotation store
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Cassidy, Steve; Johnston, Trevor. - : Stroudsburg, PA : The Association for Computational Linguistics and The Asian Federation of Natural Language Processing, 2009
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A New subtree-transfer approach to syntax-based reordering for statistical machine translation
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Abstract:
In this paper we address the problem of translating between languages with word order disparity. The idea of augmenting statistical machine translation (SMT) by using a syntax-based reordering step prior to translation, proposed in recent years, has been quite successful in improving translation quality. We present a new technique for extracting syntax-based reordering rules, which are derived through a syntactically augmented alignment of source and target texts. The parallel corpus with reordered source side is then passed to an N-gram-based machine translation system and the obtained results are contrasted with a monotone system performance. In experiments, we show significant improvement for the Chinese-to-English translation task. ; 8 page(s)
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Keyword:
080100 Artificial Intelligence and Image Processing
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URL: http://hdl.handle.net/1959.14/153934
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Improving nonparameteric Bayesian inference : experiments on unsupervised word segmentation with adaptor grammars
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Designing a citation-sensitive research tool : an initial study of browsing-specific information needs
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The Software architecture for the first challenge on generating instructions in virtual environments
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Report on the first NLG challenge on Generating Instructions in Virtual Environments (GIVE)
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Referring expression generation through attribute-based heuristics
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A Bayesian framework for word segmentation : exploring the effects of context
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