Markedness and UG in Chinese Children's Acquisition of One-word and Negative Sentences

Thepresentstudyisaninvestigationandanalysisoftherelationshipbetweenmarkednessandfirstlanguageacquisitionsequence,asshowninthecasesofone-wordandnegativesentences.Hereourobjectivesaretoargueforthepriorityofunmarkednessovermarkednessintheacquisitionsequ...

Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.

WordNet和词向量相结合的句子检索方法

针对当前句子检索方法中因数据稀疏而存在的"词不匹配"问题,提出了一种Word Net和词向量相结合的句子检索方法。首先在Word Net语义关系图中应用个性化PageRank算法计算与查询项最相关的同义词集合,实现查询项扩展,从而在一定程度上解决了查询项数据稀疏的问题;然后利用在大规模语料中训练神经网络语言模型获取的词向量对查询项和句子进行表示;最后引入WMD(word mover's distance)计算查询项与句子的语义相似度,从而利用语义信息进一步降低"词不匹配"问题带来的影响,将句子按相似度值从高到低排序作为句子检索结果。文章方法在TREC2003和TREC2004会议的项目中进行评测,MAP和R-Precision值相较于次优结果分别提高了13.29%和13.54%。

Product image sentence annotation based on kernel descriptors and tag-rank

Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.

The Comparison of Two Chinese Versions(CUV and TCV) of Bible from the Perspective of Skopos Theory

Bible has a lot of Chinese versions, among which The Chinese Union Version(CUV) and Today's Chinese Version(TCV) are most popular. The skopos of CUV is for Chinese Christians while the skopos of TCV is not just for Chinese Christians but also for the non-believers. Different target readers decide different skopos of translation. The comparison of CUV and TCV of this essay will focus on the word choices and sentence patterns to illustrate the point. Concerning the word choice, CUV uses a lot of classical words that are hard for readers today to understand. As to the sentence pattern, CUV version is generally literal translation to be loyal to God. On the contrary, because of different skopos, TCV version is very free translation. We can not deny the fact that TCV using free translation is under the influence of Nida whose dynamic equivalence is influential during the1970s especially in the Bible translation. However, from the prospective of Vermeer's theory, the prime skopos of TCV is to help non-believers understand and be willing to read Bible. Strange sentences and vague meanings will prevent them from go on reading it. Since I am also a Christian, at the end of the essay I will illustrate the limitation of both versions from a Christian's perspective.

CET写作中“有”字句负迁移到“there be存在句”的机制

汉语“有”字“话题—说明”句和英语“There引导的存在句”(existential clause,TEC)都很普遍,中国不少大学英语考生因为二者貌似,在英语写作中把前者负迁移到后者中。文章根据语言负迁移理论,主要围绕批阅CET-4/6作文时,电脑随机调出作文样本中错误的TECs,分析背后深层原因,探讨汉语的“有”字结构句子主导,以及“话题—说明”结构、标记词缺失、不分层次等机制共同对英语写作中的TEC产生负迁移问题。

An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefore,the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive,and extractive summarization.Extractive text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text,by calculating cosine similarities.Furthermore,TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique,the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

基于SentencePiece的中医学分词模型建模研究

目的:探索构建适用于中医学领域的分词模型。方法:采用基于SentencePiece的无监督学习分词方法,提出利用出版教材、名家著作及中医临床病历这3种不同类型的文献构建中医学分词模型;选择中医临床病历、名医医案作为测试集进行模型测试。结果:中医学分词模型在测试集中的Kappa系数为0.79(一致性程度很高),准确率为0.84,宏观精确率为0.84,宏观召回率为0.83,宏观f1得分为0.83。结论:所构建的分词模型对于中医学专业术语有着较好的切分效果,表明该方法可运用于中医学领域的分词模型的构建,可为进一步地研究中医学分词提供方法学参考。

The Strategies of College English Writing

This paper is to restate the importance of English writing for the Chinese students. The paper will focus on the problems exist in the students writing, such as, the misunderstood topic, the lexical mistakes, the phrase mistakes, and the coherence and unity errors. And the highlight of the paper is to deal with how to overcome the difficulties by emphasizing the elements, such as deciding on a genre of a composition, unity, and coherence for each paragraph and a whole text, the controlling idea in a paragraph and a composition and the logic realization in writing. Using proper and academic words is also necessary element in composing a text.

A Brief Research on Listening Comprehension in the Light of Effort Models in Interpreting

As English interpreting majors,we know that listening comprehension is the fundamental element of interpreting,but we still encounter a lot of listening problem,such as missing some words,misunderstanding some sentences,and so on.This paper endeavors to analyze some main problems with Daniel Gile’s Effort Models in Interpreting.

On the three-layer passage reading method in English extensive reading

This paper raises, for the first time, with a model reading passage, a three-layer passage reading method （3LPRM in brief ） in English reading： reading beyond paragraphs; reading within paragraphs and sentences, and reading with coherences of words and expressions. The paper illustrates the definition, contents, characteristics and application of the method which is easy to master and apply. The paper aims at supplying readers especially beginners of E/S/FL （English as second/foreign language） with a practical passage reading method so as to improve their reading efficiency. The paper has some practical significance and directing value.

A Contrastive Study of Word Order in Chinese and English

The present paper attempts to make a contrastive study on Chinese and English word order with a view of identifying the discrepancies and propose its significance to mutual translation.As to the research methodology,qualitative analysis and com parative analysis are adopted when the similarities and differences of the word order of English and Chinese are explained.It can be concluded that Chinese and English word order differs at the phrase level and sentence level:in terms of the discrepancies in phrase structure,they are mainly manifested in adverbial and attributive phrases;as to the discrepancies in sentence structure,they are reflected in simple,complex and special sentences.

“达”在翻译中的体现——以The 100 Simple Secrets of Healthy People的翻译为例

古今中外,“信”和“达”一直是翻译的标准。译者在翻译实践中往往注重“信”而忽略“达”。但是,“达”是译文能否被目的语读者接受的关键。笔者在对自己和陈虹两个译本进行比较时,发现翻译中的“达”主要体现在词和句两个层面。本文将从词语的释义选择和搭配,以及句子的顺序和结构调整角度来简析“达”在翻译中的体现。

Research of Paraphrasing for Chinese Complex Sentences Based on Templates

Based on the paraphrasing of Chinese simple sentences,the complex sen­tence paraphrasing by using templates are studied.Through the classifica­tion of complex sentences,syntactic analysis and structural analysis,the proposed methods construct complex sentence paraphrasing templates that the associated words are as the core.The part of speech tagging is used in the calculation of the similarity between the paraphrasing sentences and the paraphrasing template.The joint complex sentence can be divided into parallel relationship,sequence relationship,selection relationship,progres­sive relationship,and interpretive relationship’s complex sentences.The subordinate complex sentence can be divided into transition relationship,conditional relationship,hypothesis relationship,causal relationship and objective relationship’s complex sentences.Joint complex sentence and subordinate complex sentence are divided to associated words.By using pretreated sentences,the preliminary experiment is carried out to decide the threshold between the paraphrasing sentence and the template.A small scale paraphrase experiment shows the method is availability,acquire the coverage rate of paraphrasing template 40.20%and the paraphrase correct rate 62.61%.

应急语言独语句的发展钩沉与应用展望

应急语言建设是当前国家语言战略研究的重要课题。因简洁高效,独语句在应急语言服务中使用日趋广泛,形成了数量众多的“应急语言独语句”,但相关研究尚未引起重视。文章在梳理应急语言独语句发展的基础上,对应急语言独语句进行了概念界定,在此基础上对独语句在应急服务中的应用前景进行初步探讨,旨在弥补国家应急话语建设在独语句研究方面之不足,为国家应急用语的研制和编纂提供参考。

应急语言独语句的语义建构研究

独语句在应急语言中使用广泛,并形成了数量众多的应急语言独语句。从认知突显视角出发,对应急语言独语句的语义建构进行学理阐释。研究显示,应急语言独语句是概念化者在应急语境下以经济方式进行的语言编码,其本质是将应急场景中最为突显的部分前景化,促使听者能够迅速捕捉到言者所要传达的核心信息,以实现应急交际的目的。这一研究结果,不仅可以在一定程度上弥补应急语言服务对独语句研究的不足,也可以为应急语言的研制提供参考,从而更好地服务于国家应急语言建设。

基于节点词全句共现的动态词义消歧研究

文章根据词义消歧即将词义回归语境这一特性,提出了一种基于节点词全句共现的动态词义消歧方法。该方法首先以全句为窗口限定节点词的使用语境,其次使用互信息(MI)、卡方检验(χ^(2)检验)和相对词序比(RRWR)等统计方法抽取节点词的语义相关词,并参照《同义词词林》构建相关词语义范畴库,最后以共现频数作为加权系数,依靠单义词语义聚类分布率对中低频共现多义词进行消歧。采用该方法对与“美丽”共现的1030个小于7义类的多义词进行消歧的测试试验中取得了85.2%的正确率。

基于BERT字句向量与差异注意力的短文本语义匹配策略

短文本语义匹配是自然语言处理领域中的一个核心问题,可广泛应用于自动问答、搜索引擎等领域。过去的工作大多只考虑文本之间的相似部分,忽略了文本之间的差异部分,从而使模型无法充分利用到决定文本之间是否匹配的关键信息。针对上述问题,提出一种基于BERT字句向量与差异注意力的短文本语义匹配策略,利用BERT对句子对进行向量化表示,使用BiLSTM并引入多头差异注意力机制获取当前字向量与文本全局语义信息之间表征意图差异的注意力权重,结合一维卷积神经网络对句子对的语义特征向量进行降维,最后拼接字句向量并送入全连接层计算出2个句子之间的语义匹配度。通过在LCQMC和BQ Corpus数据集上的实验表明,该策略可以有效提取文本语义差异信息,从而使模型表现出更好的效果。

基于句信息增强词信息的方面级情感分类

方面级情感分类属于细粒度的情感分类,旨在判断句子中指定方面词的情感极性。近年来,句法知识在方面级情感分类任务中得到了广泛应用。目前主流的模型利用句法依存树和图卷积神经网络进行情感极性的分类。然而,此类模型着眼于利用聚合后的方面词信息来判断情感极性,很少关注句子的全局信息对情感极性的影响,从而导致情感极性分类结果出现偏差。为了解决这一问题,提出了一种基于句信息增强词信息的方面级情感分类模型,该模型通过对比学习对句向量进行表示学习,以减小句向量对比损失为目标调整词向量的特征表示,最后通过图卷积神经网络聚合意见词信息得出情感分类结果。在SemEval2014数据集和Twitter数据集上进行实验,结果表明,所提模型可以提高分类的准确性,综合验证了该方法的有效性。

网络内容的去重算法与语义量化研究

为降低网站对用户的影响,同时提升去除重复的能力,设计了一种能够应用在大型网站的去除重复的创新方案。首先,利用文本预处理技术提取网页正文内容关键词和长句特征码;其次,使用Simhash算法把特征码映射成指纹,并构建关键词指向文档的倒排索引;最后,通过关键词快速找到与待测文档高度相似的文档,接着只需比较待测文档与相似文档的指纹,即可判断网页是否重复。结果显示,该算法识别率较高,实用性良好。