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Title: | Study on the English corresponding unit of Chinese clause |
Authors: | Feng, W. Yang, Y. Li, Y. Li, X. Ren, H. |
Issue Date: | 2016 |
Publisher: | Springer Verlag |
Citation: | Study on the English corresponding unit of Chinese clause / W. Feng, Y. Yang, Y. Li, et al. — DOI 10.1007/978-3-319-50496-4_11 // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). — 2016. — Vol. 10102. — P. 129-140. |
Abstract: | This paper annotates the English corresponding units of Chinese clauses in Chinese-English translation and statistically analyzes them. Firstly, based on Chinese clause segmentation, we segment English target text into corresponding units (clause) to get a Chinese-to-English clause-aligned parallel corpus. Then, we annotate the grammatical properties of the English corresponding clauses in the corpus. Finally, we find the distribution characteristics of grammatical properties of English corresponding clauses by statistically analyzing the annotated corpus: There are more clauses (1631,74.41%) than sentences (561,25.59%); there are more major clauses (1719,78.42%) than subordinate clauses (473,21.58%); there are more adverbial clauses (392,82.88%) than attributive clauses (81,17.12%) and more non-defining clauses (358,75.69%) than restrictive relative clauses (115,24.31%) in subordinate clauses; and there are more simple clauses (1142,52.1%) than coordinate clauses (1050,47.9%). © Springer International Publishing AG 2016. |
Keywords: | CHINESE-TO-ENGLISH TRANSLATION CLAUSE ALIGNMENT CLAUSE-BASED CLAUSES DISCOURSE-BASED TRANSLATION PARALLEL CORPUS COMPUTER SCIENCE COMPUTERS LINGUISTICS NATURAL LANGUAGE PROCESSING SYSTEMS CLAUSE-BASED CLAUSES DISTRIBUTION CHARACTERISTICS NOCV1 PARALLEL CORPORA RELATIVE CLAUSE SUBORDINATE CLAUSE ARTIFICIAL INTELLIGENCE TRANSLATION (LANGUAGES) |
URI: | http://elar.urfu.ru/handle/10995/102765 |
Access: | info:eu-repo/semantics/openAccess |
SCOPUS ID: | 85003864964 |
WOS ID: | 000405476800011 |
PURE ID: | 1eeb0092-5a69-4d6d-943f-db31eb8a9db9 1381997 |
ISSN: | 3029743 |
DOI: | 10.1007/978-3-319-50496-4_11 |
Appears in Collections: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
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