Titlebook: Linguistically Motivated Statistical Machine Translation; Models and Algorithm Deyi Xiong,Min Zhang Book 2015 Springer Science+Business Med

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書目名稱Linguistically Motivated Statistical Machine Translation影響因子(影響力)




書目名稱Linguistically Motivated Statistical Machine Translation影響因子(影響力)學科排名




書目名稱Linguistically Motivated Statistical Machine Translation網絡公開度




書目名稱Linguistically Motivated Statistical Machine Translation網絡公開度學科排名




書目名稱Linguistically Motivated Statistical Machine Translation被引頻次




書目名稱Linguistically Motivated Statistical Machine Translation被引頻次學科排名




書目名稱Linguistically Motivated Statistical Machine Translation年度引用




書目名稱Linguistically Motivated Statistical Machine Translation年度引用學科排名




書目名稱Linguistically Motivated Statistical Machine Translation讀者反饋




書目名稱Linguistically Motivated Statistical Machine Translation讀者反饋學科排名

好開玩笑

Deyi Xiong,Min Zhang35. The main objective of the paper was to establish the essential (abstract) properties of the concepts of linear dependence and independence in vector spaces, and to use these for the axiomatic definition of a new algebraic object, namely the matroid. Furthermore, Whitney showed that these axioms

做方舟

Deyi Xiong,Min Zhang35. The main objective of the paper was to establish the essential (abstract) properties of the concepts of linear dependence and independence in vector spaces, and to use these for the axiomatic definition of a new algebraic object, namely the matroid. Furthermore, Whitney showed that these axioms

螢火蟲

BTG-Based SMT,the model and decoding algorithm that does not integrate language model. We then present an algorithm to integrate standard .-gram language models into the decoder. Following that, we describe two extensions to these traditional language models: a backward language model that augments the convention

軍械庫

Syntactically Annotated Reordering,sophy behind the model is that reordering under the ITG constraint is considered as a binary classification problem. We therefore build a maximum entropy classification model to predict the order . {.} whenever we apply a BTG bracketing rule to merge two neighboring phrases. As syntax knowledge can

scrutiny

巨碩

擦試不掉

Linguistically Motivated Bracketing,ntroduce a . approach in this chapter that directly determines whether a source segment can be bracketed and translated as a unit or not. We achieve this by using high-level information: syntactic and semantic structure knowledge. In the syntax-driven bracketing model, we employ syntactic knowledge

grieve

Translation Rule Selection with Document-Level Semantic Information,slation rule selection is a task of selecting appropriate translation rules for an ambiguous source-language segment. We represent the gist of a document as the topic of the document. Therefore we introduce two topic-based models for translation rule selection which incorporates global topic informa

helper-T-cells

Translation Error Detection with Linguistic Features,chine translation quality. The previous work is largely based on confidence estimation using system-based features, such as word posterior probabilities calculated from .-best lists or word lattices. We propose to incorporate two groups of linguistic features, which convey information from outside m