The C-E Translation Strategies of Run-on Sentences

Run-on sentences are used frequently in daily life and literature;however,there is no corresponding sentence pattern in English.Therefore,it is challenging for translators to render run-on sentences into idiomatic English.This paper introduces the definition,classification,and the translation of run-on sentences,aiming to summarize the common rules of its translation.

Sentence及其结构研究——以古典主义时期作品为例

文章以sentence为研究对象,一方面对国内外曲式学教材及相关文献展开概念比较、辨析与理论梳理,阐明其基本概念与内部结构;另一方面则以古典主义时期作品为分析实例,讨论这种特定主题(句法)的“结构范型”、并进一步对其“结构变形”的情况进行归类。

基于Sentence-BERT的专利技术主题聚类研究——以人工智能领域为例

[研究目的]将Sentence-BERT模型应用于专利技术主题聚类,解决专利文献为突出新颖性,常使用独特技术术语造成词汇向量语义特征稀疏的问题。[研究方法]以人工智能领域2015年-2019年的22370篇专利为实验数据。首先,采用Sentence-BERT算法对专利文献摘要文本进行向量化表示;其次,对向量化矩阵进行数据降维,利用HDBSCAN方式寻找原始数据中的高密度簇;最后,识别类簇文本集合中的主题特征,并完成主题呈现。[研究结论]对比LDA主题模型、K-means、doc2vec等方法,本文的实验结果提高了主题划分的细粒度和精确度,获得了较好的主题一致性。如何采用fine-tune策略进一步提升模型的效果,是未来该方法进一步深入探索的方向。

Review of Research on English Translation of Chinese Running Sentences

In order to convey complete meanings,there is a phenomenon in Chinese of using multiple running sentences.Xu Jingning(2023,p.66)states,“In communication,a complete expression of meaning often requires more than one clause,which is common in human languages.”Domestic research on running sentences includes discussions on defining the concept and structural features of running sentences,sentence properties,sentence pattern classifications and their criteria,as well as issues related to translating running sentences into English.This article primarily focuses on scholarly research into the English translation of running sentences in China,highlighting recent achievements and identifying existing issues in the study of running sentence translation.However,by reviewing literature on the translation of running sentences,it is found that current research in the academic community on non-core running sentences is limited.Therefore,this paper proposes relevant strategies to address this issue.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

融合Sentence-BERT和LDA的评论文本主题识别

[目的/意义]为了解决评论文本主题识别时语义描述不充分以及学习到的主题语义连贯性不强等问题。本文将Sentence-BERT句子嵌入模型和LDA模型相结合,提升评论文本主题的语义性。[方法/过程]采用Sentence-BERT模型获取评论文本句子层面的向量特征,同时,采用LDA模型获取评论文本的概率主题向量,随后使用自动编码器连接两组向量,运用K-means算法对潜在空间向量进行聚类,从类簇中获取上下文主题信息。[结果/结论]通过对评论文本数据集的实验,本文方法可以较好地获得具有语义信息的主题词。Sentence-BERT模型与LDA结合,增加了模型的复杂性。通过对比,本文方法获得的主题一致性指标(Coherence)优于目前常见的评论文本主题识别方法。

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.

A Sentence Retrieval Generation Network Guided Video Captioning

Currently,the video captioning models based on an encoder-decoder mainly rely on a single video input source.The contents of video captioning are limited since few studies employed external corpus information to guide the generation of video captioning,which is not conducive to the accurate descrip-tion and understanding of video content.To address this issue,a novel video captioning method guided by a sentence retrieval generation network(ED-SRG)is proposed in this paper.First,a ResNeXt network model,an efficient convolutional network for online video understanding(ECO)model,and a long short-term memory(LSTM)network model are integrated to construct an encoder-decoder,which is utilized to extract the 2D features,3D features,and object features of video data respectively.These features are decoded to generate textual sentences that conform to video content for sentence retrieval.Then,a sentence-transformer network model is employed to retrieve different sentences in an external corpus that are semantically similar to the above textual sentences.The candidate sentences are screened out through similarity measurement.Finally,a novel GPT-2 network model is constructed based on GPT-2 network structure.The model introduces a designed random selector to randomly select predicted words with a high probability in the corpus,which is used to guide and generate textual sentences that are more in line with human natural language expressions.The proposed method in this paper is compared with several existing works by experiments.The results show that the indicators BLEU-4,CIDEr,ROUGE_L,and METEOR are improved by 3.1%,1.3%,0.3%,and 1.5%on a public dataset MSVD and 1.3%,0.5%,0.2%,1.9%on a public dataset MSR-VTT respectively.It can be seen that the proposed method in this paper can generate video captioning with richer semantics than several state-of-the-art approaches.

A Study of Nominal Predicate Sentences Under the Framework of Cognitive Grammar

Cognitive grammar,as a linguistic theory that attaches importance to the relationship between language and thinking,provides us with a more comprehensive way to understand the structure,semantics and cognitive processing of noun predicate sentences.Therefore,under the framework of cognitive grammar,this paper tries to analyze the semantic connection and cognitive process in noun predicate sentences from the semantic perspective and the method of example theory,and discusses the motivation of the formation of this construction,so as to provide references for in-depth analysis of the cognitive laws behind noun predicate sentences.

汉语的“句子”与英语的sentence

汉语的"句子"不等于英语的sentence,它更像utterance。在以英语研究为代表的西方语言研究中,sentence是一个抽象单位。而汉语研究一向注重实际使用的单位,忽视抽象单位,特别是对相当于sentence这一级的研究开始较晚,以至汉语的"句子"至今仍是一个具体单位,或称"动态单位"。跟sentence相当的汉语单位是"词组",不是"句子"。"词组"或称"短语"是汉语最大的结构单位、语法单位或"静态单位"。

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.

基于Sentence-BERT语义表示的咨询问题提示列表自动构建方法研究——以糖尿病咨询为例

[目的/意义]咨询问题提示列表能引导咨询者在智能问答和智能咨询系统进行咨询并为动态咨询引导提供基础。目前,关于问题提示列表构建的研究大多采用专家咨询法、访谈法,这些方法无法满足智能咨询服务要求,本文以有问必答网中糖尿病问答为例,提出了基于Sentence-BERT语义表示的咨询问题提示列表自动构建模型。[方法/过程]本文首先在糖尿病相关文献调查和分析的基础上确定糖尿病类目体系,并人工标注咨询问题类别;其次使用LDA模型对每类问题集进行主题聚类;然后各主题下通过Sentence-BERT预训练模型进行问题语义表示,textRank算法计算问题重要性并排序;最终冗余处理后构建出咨询问题提示列表。[结果/结论]实验结果表明,本文提出的模型能有效构建出信息质量较高的、内容丰富的咨询问题提示列表,对咨询引导有促进作用。

Rethinking English periodic sentence

Periodic sentence is often identified as sentence with main clause as end-weight. In fact, this definition is so confusing that it causes the same confusion in practice. This paper aims at rethinking periodic sentence and advocates the adoption of noble styles with periodic sentence as its chief representative.

On Sentence Complexity in THE TIMES: A Comparative Study of SentenceLength and Sentence Complexity in the News Section and the Sports Section

My investigation will serve two purposes. First, I shall investigate the function of the subclauses in the corpus in relation to their complexity, and I shall establish whether there is a correlation between sentence length and sentence complexity.Second, I shall analyse the complexity of the subclauses collected from the two sections and compare the results from these sections, focusing on finite subclauses and non-finite subclauses. I hope to be able to point out some differences in style between the news and sports sections concerning the use of subordinate clauses in various syntactic functions in order to examine how the choice of linguistic structures differs in different sections of The Times.

Developing Sentence Sense

There is a big problem in understanding long sentences which are complex and complicated in English for many people.The desire seems obvious that people have difficulties using a complete sentence. So this paper is to solve the problems mentionedby developing sentence sense and a chart with much help.

基于Sentence-Rank的图像句子标注

传统的图像语义句子标注是利用句子模板完成对图像内容描述,但其标注句子很难做到符合语言逻辑。针对这一问题,提出基于统计思想从语料库中选出一条最优的句子来描述图像内容,设计以N-gram算法为主要思想的Sentence-Rank算法生成标注句子。首先执行机器视觉特征学习,选择标注性能最好的HSV-LBP-HOG融合特征完成图像分类,获得图像标注关键词。然后,利用字符串匹配算法从语料库中列出包含所有标注关键词的句子,并将得到的句子通过Sentence-Rank算法进行价值排序,选取评分最高的句子描述图像。实验结果表明,该方法得到的标注句子具有较低的困惑度,较好地解决了句子的语言逻辑问题。

Masked Sentence Model Based on BERT for Move Recognition in Medical Scientific Abstracts

Purpose:Mo ve recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units.To improve the performance of move recognition in scientific abstracts,a novel model of move recognition is proposed that outperforms the BERT-based method.Design/methodology/approach:Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences.In this paper,inspired by the BERT masked language model(MLM),we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition.Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps.Then,we compare our model with HSLN-RNN,BERT-based and SciBERT using the same dataset.Findings:Compared with the BERT-based and SciBERT models,the F1 score of our model outperforms them by 4.96%and 4.34%,respectively,which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-theart results of HSLN-RNN at present.Research limitations:The sequential features of move labels are not considered,which might be one of the reasons why HSLN-RNN has better performance.Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed,which is a typical biomedical database,to fine-tune our model.Practical implications:The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences.Originality/value:T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way.The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.

CiteOpinion: Evidence-based Evaluation Tool for Academic Contributions of Research Papers Based on Citing Sentences

Purpose:To uncover the evaluation information on the academic contribution of research papers cited by peers based on the content cited by citing papers,and to provide an evidencebased tool for evaluating the academic value of cited papers.Design/methodology/approach:CiteOpinion uses a deep learning model to automatically extract citing sentences from representative citing papers;it starts with an analysis on the citing sentences,then it identifies major academic contribution points of the cited paper,positive/negative evaluations from citing authors and the changes in the subjects of subsequent citing authors by means of Recognizing Categories of Moves(problems,methods,conclusions,etc.),and sentiment analysis and topic clustering.Findings:Citing sentences in a citing paper contain substantial evidences useful for academic evaluation.They can also be used to objectively and authentically reveal the nature and degree of contribution of the cited paper reflected by citation,beyond simple citation statistics.Practical implications:The evidence-based evaluation tool CiteOpinion can provide an objective and in-depth academic value evaluation basis for the representative papers of scientific researchers,research teams,and institutions.Originality/value:No other similar practical tool is found in papers retrieved.Research limitations:There are difficulties in acquiring full text of citing papers.There is a need to refine the calculation based on the sentiment scores of citing sentences.Currently,the tool is only used for academic contribution evaluation,while its value in policy studies,technical application,and promotion of science is not yet tested.

A Sentence Similarity Estimation Method Based on Improved Siamese Network

In this paper we employ an improved Siamese neural network to assess the semantic similarity between sentences. Our model implements the function of inputting two sentences to obtain the similarity score. We design our model based on the Siamese network using deep Long Short-Term Memory (LSTM) Network. And we add the special attention mechanism to let the model give different words different attention while modeling sentences. The fully-connected layer is proposed to measure the complex sentence representations. Our results show that the accuracy is better than the baseline in 2016. Furthermore, it is showed that the model has the ability to model the sequence order, distribute reasonable attention and extract meanings of a sentence in different dimensions.

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

Thepresentstudyisaninvestigationandanalysisoftherelationshipbetweenmarkednessandfirstlanguageacquisitionsequence,asshowninthecasesofone-wordandnegativesentences.Hereourobjectivesaretoargueforthepriorityofunmarkednessovermarkednessintheacquisitionsequ...