平行语料库检索软件SDAU-ParaConc设计与实现

当代语言研究离不开语料库,对语料库的检索需要计算机软件支持,但平行语料库检索软件数量极少,且存在不符合中国国情的情况。因此,有必要开发一款符合中国国情、适应大数据检索需求、减少语言研究中重复劳动的平行语料库检索软件。在考察现有4款平行语料库检索软件,对比分析它们各自的优势及不足后,结合中国国情及语言研究实践经验,提出了新的平行语料库检索软件SDAU-ParaConc设计理念。介绍了SDAU-ParaConc的实现方式与特点。与之前的软件对比结果表明,SDAU-ParaConc操作步骤平均简化了60%,检索效率平均提升了9.5%。     

在课堂教学中使用语料库:其优点与需要注意的问题

本从教学实用的角度重新就使用语料库的正、反两方面的观点作了探讨与评论。作认为语料库有可能成为语言教学中最为有用的工具之一，但若要充分发挥其作用，则仍然需要广大研究人员、教师与语料库设计人员的共同努力。中还认为，学习语料库对中国的英语学习将会十分有用。     

对基于语料库的英语口语中模糊限制语的对比研究

模糊限制语是一种常见的语言现象,在语言表达中可以使意思更加模糊或者精确。通过中外两个语料库的对比研究发现：中国英语学习者使用的模糊限制语的类型少,过度依赖少数的常用词,而多数模糊限制语的使用频率很低。在英语口语中应有意识地使用不同的模糊限制语以达到更好的交流的目的。     

Automatic extraction of bilingual word pairs using inductive chain learning in various languages

In this paper, we propose a new learning method for extracting bilingual word pairs from parallel corpora in various languages. In cross-language information retrieval, the system must deal with various languages. Therefore, automatic extraction of bilingual word pairs from parallel corpora with various languages is important. However, previous works based on statistical methods are insufficient because of the sparse data problem. Our learning method automatically acquires rules, which are effective to solve the sparse data problem, only from parallel corpora without any prior preparation of a bilingual resource (e.g., a bilingual dictionary, a machine translation system). We call this learning method Inductive Chain Learning (ICL). Moreover, the system using ICL can extract bilingual word pairs even from bilingual sentence pairs for which the grammatical structures of the source language differ from the grammatical structures of the target language because the acquired rules have the information to cope with the different word orders of source language and target language in local parts of bilingual sentence pairs. Evaluation experiments demonstrated that the recalls of systems based on several statistical approaches were improved through the use of ICL.     

]语料库建设中的版权问题探究

国内语料库建设已有多年,取得了丰硕成果,但其版权问题一直制约着我国语料库建设的发展。文章按照＂语料库建设现状——分析存在的具体版权问题——综合列举国内外各种解决的办法——适合我国语料库版权保护的具体措施＂的思路来探究语料库建设中的版权问题。     

An Empirical Research on DDL in L2 Writing

L2 （second language） writing teaching and research is greatly influenced by the advent of computer technology. This paper reviews papers, in the field of DDL （data-driven learning） in L2 writing, published in international journals and in Chinese journals, and differences are found in research methodology, research perspective, and research realm. Based on the analysis of the differences, the study aims at finding out the effects of the application of DDL in L2 teaching classroom. The present study, employing semi-structured writing and learning journals, explores how English learners perceive the use of corpora in their writing process. Based on the statistics, it is concluded that DDL in L2 writing is beneficial for vocabulary acquisition. However, corpus consultation does not work well for all learners. Learners＇ attitudes toward the use of corpus are various. The major findings may find application in teaching practice, and implications for teaching are discussed.     

语法学研究的学科对象和语料对象

语法学的研究对象可以分为学科对象和语料对象。学科对象应以句子为研究基点，任务是找出语法单位结构和功能的规律。语料对象是普通话，语法研究应该区分不同层次的普通话语料。例如，口语和书面语就应该分别进行研究，这样才能得出正确的结论。     

语义韵研究在外语教学中的应用

语义韵是语言学研究的新对象,语义韵研究在词典编纂、翻译、修辞和外语教学等方面的应用前景广阔。在外语教学中,语义韵研究拓展了词汇教学的内容,增加了同义词辨析的维度,提高了学生的交际能力,促进了词汇教学方法的创新。     

语料库方法在语用学研究中的运用

提要:语料库方法被称为现代语言学研究的三大方法之一,本文基于调查发现,该方法在国外语用学研究中的运用比例相对较高,而在国内运用比例却总体偏低,这一局面与国内语用学研究的主要话题和分析单位的性质有一定的联系。要拓展该方法在国内语用学领域的应用,需要适当回归语言本体,更多地关注语言的语用属性研究。另一方面,与国内相关研究主要关注二语学习者对语用标记语的习得情况不同,国外采用语料库方法开展的研究涉及的话题范围更为宽泛,新词新义、幽默、指示语、言语行为的语调、隐喻和转喻等话题都在考察之列,这一发现对未来中国语用学研究具有借鉴意义。     

Focused web crawling in the acquisition of comparable corpora

Cross-Language Information Retrieval (CLIR) resources, such as dictionaries and parallel corpora, are scarce for special domains. Obtaining comparable corpora automatically for such domains could be an answer to this problem. The Web, with its vast volumes of data, offers a natural source for this. We experimented with focused crawling as a means to acquire comparable corpora in the genomics domain. The acquired corpora were used to statistically translate domain-specific words. The same words were also translated using a high-quality, but non-genomics-related parallel corpus, which fared considerably worse. We also evaluated our system with standard information retrieval (IR) experiments, combining statistical translation using the Web corpora with dictionary-based translation. The results showed improvement over pure dictionary-based translation. Therefore, mining the Web for comparable corpora seems promising.