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Titlebook: Computational Linguistics and Intelligent Text Processing; 13th International C Alexander Gelbukh Conference proceedings 2012 Springer-Verl

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https://doi.org/10.1007/978-94-6209-488-8nimum Bayes Risk decoding in order to quantify the relative performance within lattice or n-best output with regard to the 1-best output. In contrast, our approach is to enlarge the hypothesis space in order to incorporate the combinatorial nature of MBR decoding. In this setting, we perform experim
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Gaining a Critical Perspective,onstructs in Hindi. The performance of existing translation system makes the point clear that there exists no satisfactorily efficient Noun Compound translation tool from English to Hindi although the need of one is unprecedented in the context of machine translation. In this paper we integrate Noun
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https://doi.org/10.1007/978-3-031-66913-2y extract and to align neoclassical compounds in two languages from comparable corpora. In order to do this, we assume that neoclassical compounds translate compositionally to neoclassical compounds from one language to another. The method covers the two main forms of neoclassical compounds and is s
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https://doi.org/10.1007/978-3-031-66913-2n retrieval (IR) domain and more specifically, from question-answering systems (QAS). By analogy to QAS, we consider a word to be translated as a part of a question extracted from a source language, and we try to find out the correct translation assuming that it is contained in the correct answer of
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https://doi.org/10.1007/978-94-6300-408-4source text. We are using this methodology for the enlargement of the Spanish and Catalan versions of WordNet 3.0, but the methodology can also be used for other languages. As big parallel corpora with semantic annotation are not usually available, we explore two strategies to overcome this problem:
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Mathematics is a Powerful Tool,tem users. Some of the MT architectures are able to incorporate bilingual noun lexica, which can improve the translation quality of sentences which include nouns. In this paper, we proposed an automatic method to enable a monolingual user to add new words to the lexicon. In the experiments, we compa
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https://doi.org/10.1007/978-3-030-23467-6 speed up document search and reduce classifier training times. The data available to us is frequently divided into several broad domains with many sub-category levels. Each of these domains of data constitutes a subspace which can be processed separately. In this paper, separate classifiers of the
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