由于不同时期的录波数据记录标准有所不同,以及各个生产厂家对标准的解读存在偏差,造成同源录波数据的通道名称存在个性化差异,且通道索引号不同,难以进行录波数据的同源匹配。针对上述问题,提出基于句向量掩码纠错双向编码器表征语言模...由于不同时期的录波数据记录标准有所不同,以及各个生产厂家对标准的解读存在偏差,造成同源录波数据的通道名称存在个性化差异,且通道索引号不同,难以进行录波数据的同源匹配。针对上述问题,提出基于句向量掩码纠错双向编码器表征语言模型(sentence-masked language model as correction bidirectional encoder representations from transformers,Sentence-MacBERT)的同源录波数据匹配方法。首先,分析录波文件的记录格式特点,根据录波文件的格式特点完成核查信息表的构建。然后,通过构建的核查信息表进行录波文件自动校核。最后,在双向编码器表征(bidirectional encoder representations from transformers,BERT)模型的基础上构建Sentence-MacBERT同源通道匹配模型,完成同源录波数据匹配。算例分析表明,根据核查信息表能够完成录波文件的自动校核,并对解析失败的录波文件发出告警信息。利用Sentence-MacBERT模型进行通道名称匹配的效果良好,能够有效地完成录波数据的同源匹配,帮助运行人员进行故障分析。展开更多
I.What are topic sentences in a passage?A topic sentence is the main idea of a paragraph.It tells readers what the paragraph will talk about.Usually,the topic sentence is the first sentence of a paragraph,but sometime...I.What are topic sentences in a passage?A topic sentence is the main idea of a paragraph.It tells readers what the paragraph will talk about.Usually,the topic sentence is the first sentence of a paragraph,but sometimes it can come in the middle or at the end.A good topic sentence helps readers understand the writer's purpose quickly.展开更多
本文以2026年托福考试新增题型“Build a Sentence”为研究对象,基于官方样题探讨其设计逻辑、测试学依据和语言学理论基础。本文认为,该题型通过词序重构测评考生的句法能力和语用能力,符合交际语言测试的真实性原则。文章结合生成语...本文以2026年托福考试新增题型“Build a Sentence”为研究对象,基于官方样题探讨其设计逻辑、测试学依据和语言学理论基础。本文认为,该题型通过词序重构测评考生的句法能力和语用能力,符合交际语言测试的真实性原则。文章结合生成语法与认知语法视角,分析了题型所涉及的核心语法点及学习者的典型错误来源,并提出分层教学框架与预防性训练策略。研究结果为语言测评效度验证与教学实践提供参考。展开更多
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 summariza...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.展开更多
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...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.展开更多
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 ...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.展开更多
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...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.展开更多
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,...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.展开更多
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...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.展开更多
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 ou...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.展开更多
文摘由于不同时期的录波数据记录标准有所不同,以及各个生产厂家对标准的解读存在偏差,造成同源录波数据的通道名称存在个性化差异,且通道索引号不同,难以进行录波数据的同源匹配。针对上述问题,提出基于句向量掩码纠错双向编码器表征语言模型(sentence-masked language model as correction bidirectional encoder representations from transformers,Sentence-MacBERT)的同源录波数据匹配方法。首先,分析录波文件的记录格式特点,根据录波文件的格式特点完成核查信息表的构建。然后,通过构建的核查信息表进行录波文件自动校核。最后,在双向编码器表征(bidirectional encoder representations from transformers,BERT)模型的基础上构建Sentence-MacBERT同源通道匹配模型,完成同源录波数据匹配。算例分析表明,根据核查信息表能够完成录波文件的自动校核,并对解析失败的录波文件发出告警信息。利用Sentence-MacBERT模型进行通道名称匹配的效果良好,能够有效地完成录波数据的同源匹配,帮助运行人员进行故障分析。
文摘I.What are topic sentences in a passage?A topic sentence is the main idea of a paragraph.It tells readers what the paragraph will talk about.Usually,the topic sentence is the first sentence of a paragraph,but sometimes it can come in the middle or at the end.A good topic sentence helps readers understand the writer's purpose quickly.
文摘本文以2026年托福考试新增题型“Build a Sentence”为研究对象,基于官方样题探讨其设计逻辑、测试学依据和语言学理论基础。本文认为,该题型通过词序重构测评考生的句法能力和语用能力,符合交际语言测试的真实性原则。文章结合生成语法与认知语法视角,分析了题型所涉及的核心语法点及学习者的典型错误来源,并提出分层教学框架与预防性训练策略。研究结果为语言测评效度验证与教学实践提供参考。
基金The National Natural Science Foundation of China(No.61133012)the Humanity and Social Science Foundation of the Ministry of Education(No.12YJCZH274)+1 种基金the Humanity and Social Science Foundation of Jiangxi Province(No.XW1502,TQ1503)the Science and Technology Project of Jiangxi Science and Technology Department(No.20121BBG70050,20142BBG70011)
文摘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.
文摘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.
文摘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.
文摘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.
基金supported by the project “The demonstration system of rich semantic search application in scientific literature” (Grant No. 1734) from the Chinese Academy of Sciences
文摘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.
文摘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.
文摘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.
