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
Objective: This paper reviewed and examined the quality of all the qualitative evaluation studies indexed by two key search terms of “qualitative” and “evaluation” in the Social Work Abstracts database from 1990 t...Objective: This paper reviewed and examined the quality of all the qualitative evaluation studies indexed by two key search terms of “qualitative” and “evaluation” in the Social Work Abstracts database from 1990 to 2003 against a number of criteria typically adopted in the field of qualitative research. The review led to a dissatisfactory finding of the low quality of many qualitative evaluation studies due to their insensitivity to the following issues: philosophical basis of the study, auditability (detailed documentation of the participants and data collecting procedure), biases (acknowledgement of biases and preoccupation, and steps to deal with them), credibility or trustworthiness (triangulation, peer checking and participant verification of the findings), consistency (reliability consciousness and audit trails), and critical interpretation of the data (alternative explanations, disconfirming evidence, and limitations of the study). It was recommended that researchers be cautious when utilizing findings from the published qualitative evaluation studies; that social workers be sensitive to the issue of quality when conducting qualitative evaluation studies; that researchers be critical when judging the qualitative evaluation studies in social work; that researchers develop a clear set of guidelines for qualitative studies; that social work training institutes design qualified qualitative research courses; that a database of social work in China be established; that researchers be engaged in more qualitative studies that demonstrate high quality; that myths in qualitative research be debunked; and that adequate training for social workers on qualitative evaluation studies be provided.展开更多
Researchers around the world strive to communicate new knowledge,primarily via publication,with the abstract being crucial in conveying core insights.Previous research has generally analyzed the discourse features of ...Researchers around the world strive to communicate new knowledge,primarily via publication,with the abstract being crucial in conveying core insights.Previous research has generally analyzed the discourse features of abstracts from a macro perspective and often employed either outdated texts,such as those over a decade old,or papers written by authors with lower English academic writing proficiency as research material.In this study,we analyzed forty abstracts from leading journals in applied linguistics,evenly split between Chinese and international journals.It revealed that the use of nominalization in abstracts by Chinese and international scholars showed similarities due to the universal academic requirement for conciseness.However,due to cultural and educational differences,each group differed in their respective language choices and nominalization usage.By analyzing the application of nominalization in different cultural contexts,the results of our study offered practical suggestions for crafting abstracts that effectively convey information,thereby,contributing to the broader academic community.展开更多
Purpose:Automatic keyphrase extraction(AKE)is an important task for grasping the main points of the text.In this paper,we aim to combine the benefits of sequence labeling formulation and pretrained language model to p...Purpose:Automatic keyphrase extraction(AKE)is an important task for grasping the main points of the text.In this paper,we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research.Design/methodology/approach:We regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT,which was released by Google in 2018.We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain,which contains 100,000 abstracts as training set,6,000 abstracts as development set and 3,094 abstracts as test set.We use unsupervised keyphrase extraction methods including term frequency(TF),TF-IDF,TextRank and supervised machine learning methods including Conditional Random Field(CRF),Bidirectional Long Short Term Memory Network(BiLSTM),and BiLSTM-CRF as baselines.Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models.Findings:Compared with character-level BiLSTM-CRF,the best baseline model with F1 score of 50.16%,our character-level sequence labeling model based on BERT obtains F1 score of 59.80%,getting 9.64%absolute improvement.Research limitations:We just consider automatic keyphrase extraction task rather than keyphrase generation task,so only keyphrases that are occurred in the given text can be extracted.In addition,our proposed dataset is not suitable for dealing with nested keyphrases.Practical implications:We make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts(CAKE)publicly available for the benefits of research community,which is available at:https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction.Originality/value:By designing comparative experiments,ou展开更多
During oncogenesis,the hyper-activation of proto-oncogenes and defection of tumor suppressor genes（Zhao et al.,2012,2016a）can regulate cell proliferation,differentiation,apoptosis,and cell-to-cell communication（Bal...During oncogenesis,the hyper-activation of proto-oncogenes and defection of tumor suppressor genes（Zhao et al.,2012,2016a）can regulate cell proliferation,differentiation,apoptosis,and cell-to-cell communication（Balmain et al.,2003;Haber and Settleman,2007）.Recent evidence has shown that non-coding RNAs, such as microRNAs （miRNAs） （Chen, 2005）, and long non- coding RNAs （lncRNAs）, can also act as oncogenes to initiate and promote cancer progression.展开更多
基金Financial support was provided by the following sources to convene a meeting of the CONSORT Group in Montebello Canada+9 种基金in January 2007:the American Society of Clinical OncologyBMJCanadian Institutes for Health ResearchJohnson & JohnsonThe LancetNordic Cochrane CentrePLoS Medicine UK Cochrane Centreand UK National Co-ordinating Centre for Research Methodology.DM is supported by a University of Ottawa Research Chair.国家重点基础研究发展计划(973计划)项目资助(No.2006CB504602)。
文摘背景:对于与随机对照试验(randomized controlled trial,RCT)有关的学术会议论文或期刊中发表的文章来说,清楚、明了、信息量充足的摘要是十分重要的,因为读者经常仅仅根据报告的摘要对一个临床试验作出评价。为此,我们需要对"临床试验报告的统一标准(Consolidated Standards of Reporting Trials,CONSORT)声明"进行扩充,制定一个期刊与学术会议论文摘要中报告RCT的必备条目清单。今后在任何期刊发表的论文或学术会议论文摘要中,作者对RCT结果的报告都要包含这些内容。方法与结果:我们根据现有的质量评价工具和基于经验的证据总结出一个条目清单。运用三轮修正式德尔菲法(modified-Delphi process)进行条目筛选。邀请共计109人参与电子网络调查,反馈率为61%。调查结果于2007年1月在加拿大蒙特贝罗举行的CONSORT小组会议中公布,与会的26人中有临床试验实施人员、统计学家、流行病学家以及生物医学编辑。经过讨论最终确定条目,随后对其进行修订以保证这些条目体现了会议期间以及会后的讨论思路。摘要CONSORT建议RCT报告的摘要需要有一个结构化的格式,其中应该包括具体的试验目的、试验设计(随机分配的方法、盲法或遮蔽等)、研究对象(对象描述、随机分组的样本量以及用于分析的样本量)、每组实施的干预、实施的干预对主要疗效结果的影响及其危害、试验结论、试验注册名称和编号以及资金来源。本文对每一条能够找到例子的纳入条目都配有良好报告范例、基本原理以及证据等,十分明了易懂,因此我们建议与清单同时使用。结论:摘要CONSORT旨在改善期刊与学术会议中发表的论文摘要的质量,这将有助于摘要提供详尽清晰的信息,这些信息能够帮助读者对试验的有效性和试验结果的适用性做出正确的评价。
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Objective: This paper reviewed and examined the quality of all the qualitative evaluation studies indexed by two key search terms of “qualitative” and “evaluation” in the Social Work Abstracts database from 1990 to 2003 against a number of criteria typically adopted in the field of qualitative research. The review led to a dissatisfactory finding of the low quality of many qualitative evaluation studies due to their insensitivity to the following issues: philosophical basis of the study, auditability (detailed documentation of the participants and data collecting procedure), biases (acknowledgement of biases and preoccupation, and steps to deal with them), credibility or trustworthiness (triangulation, peer checking and participant verification of the findings), consistency (reliability consciousness and audit trails), and critical interpretation of the data (alternative explanations, disconfirming evidence, and limitations of the study). It was recommended that researchers be cautious when utilizing findings from the published qualitative evaluation studies; that social workers be sensitive to the issue of quality when conducting qualitative evaluation studies; that researchers be critical when judging the qualitative evaluation studies in social work; that researchers develop a clear set of guidelines for qualitative studies; that social work training institutes design qualified qualitative research courses; that a database of social work in China be established; that researchers be engaged in more qualitative studies that demonstrate high quality; that myths in qualitative research be debunked; and that adequate training for social workers on qualitative evaluation studies be provided.
基金supported by the Serbia’s Ministry of Science,Technological Development and Innovation(Contract No.451-03-65/2024-03/200156)the Faculty of Technical Sciences,University of Novi Sad,Serbia through the project“Scientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences,University of Novi Sad”(No.01-3394/1)。
文摘Researchers around the world strive to communicate new knowledge,primarily via publication,with the abstract being crucial in conveying core insights.Previous research has generally analyzed the discourse features of abstracts from a macro perspective and often employed either outdated texts,such as those over a decade old,or papers written by authors with lower English academic writing proficiency as research material.In this study,we analyzed forty abstracts from leading journals in applied linguistics,evenly split between Chinese and international journals.It revealed that the use of nominalization in abstracts by Chinese and international scholars showed similarities due to the universal academic requirement for conciseness.However,due to cultural and educational differences,each group differed in their respective language choices and nominalization usage.By analyzing the application of nominalization in different cultural contexts,the results of our study offered practical suggestions for crafting abstracts that effectively convey information,thereby,contributing to the broader academic community.
基金This work is supported by the project“Research on Methods and Technologies of Scientific Researcher Entity Linking and Subject Indexing”(Grant No.G190091)from the National Science Library,Chinese Academy of Sciencesthe project“Design and Research on a Next Generation of Open Knowledge Services System and Key Technologies”(2019XM55).
文摘Purpose:Automatic keyphrase extraction(AKE)is an important task for grasping the main points of the text.In this paper,we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research.Design/methodology/approach:We regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT,which was released by Google in 2018.We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain,which contains 100,000 abstracts as training set,6,000 abstracts as development set and 3,094 abstracts as test set.We use unsupervised keyphrase extraction methods including term frequency(TF),TF-IDF,TextRank and supervised machine learning methods including Conditional Random Field(CRF),Bidirectional Long Short Term Memory Network(BiLSTM),and BiLSTM-CRF as baselines.Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models.Findings:Compared with character-level BiLSTM-CRF,the best baseline model with F1 score of 50.16%,our character-level sequence labeling model based on BERT obtains F1 score of 59.80%,getting 9.64%absolute improvement.Research limitations:We just consider automatic keyphrase extraction task rather than keyphrase generation task,so only keyphrases that are occurred in the given text can be extracted.In addition,our proposed dataset is not suitable for dealing with nested keyphrases.Practical implications:We make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts(CAKE)publicly available for the benefits of research community,which is available at:https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction.Originality/value:By designing comparative experiments,ou
文摘During oncogenesis,the hyper-activation of proto-oncogenes and defection of tumor suppressor genes（Zhao et al.,2012,2016a）can regulate cell proliferation,differentiation,apoptosis,and cell-to-cell communication（Balmain et al.,2003;Haber and Settleman,2007）.Recent evidence has shown that non-coding RNAs, such as microRNAs （miRNAs） （Chen, 2005）, and long non- coding RNAs （lncRNAs）, can also act as oncogenes to initiate and promote cancer progression.
