Learning Algorithms for Keyphrase Extraction

Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by GenEx suggests that about 80% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications.     

中文生物医学关键词-主题词映射表计算机辅助构建与维护机制研究

将自然语言应用到信息组织、标引、检索和分析所需的各种词表中,实现自然语言与受控语言间的互操作,是新一代知识组织系统构建模式。文章围绕“关键词识别与扩充、关键词-主题词映射关系构建、关键词-主题词映射关系更新”3个方面,介绍了中文生物医学关键词-主题词映射表的计算机辅助构建与维护机制,以及系统的功能架构。     

Applying regression models to query-focused multi-document summarization

Most existing research on applying machine learning techniques to document summarization explores either classification models or learning-to-rank models. This paper presents our recent study on how to apply a different kind of learning models, namely regression models, to query-focused multi-document summarization. We choose to use Support Vector Regression (SVR) to estimate the importance of a sentence in a document set to be summarized through a set of pre-defined features. In order to learn the regression models, we propose several methods to construct the “pseudo” training data by assigning each sentence with a “nearly true” importance score calculated with the human summaries that have been provided for the corresponding document set. A series of evaluations on the DUC data sets are conducted to examine the efficiency and the robustness of the proposed approaches. When compared with classification models and ranking models, regression models are consistently preferable.     

动觉机理的研究进展

文章通过对动觉机理的讨论,明确地界定了它的定义;对动觉产生的生理机制,与动觉相关因素的以及国内外动觉机理研究的进展进一步分析和综述。得到的结果是国内外有关动觉的研究领域杂乱;实践操作性的研究过多,理论方面的研究偏少;研究的方法单一、片面;我国的动觉研究不但落后于国外的研究很远,而且与实际的应用联系不紧密。     

MRC-Sum: An MRC framework for extractive summarization of academic articles in natural sciences and medicine

Extractive summarization for academic articles in natural sciences and medicine has attracted attention for a long time. However, most existing extractive summarization models often process academic articles with sentence classification models, which are hard to produce comprehensive summaries. To address this issue, we explore a new view to solve the extractive summarization of academic articles in natural sciences and medicine by taking it as a question-answering process. We propose a novel framework, MRC-Sum, where the extractive summarization for academic articles in natural sciences and medicine is cast as an MRC (Machine Reading Comprehension) task. To instantiate MRC-Sum, article-summary pairs in the summarization datasets are firstly reconstructed into (Question, Answer, Context) triples in the MRC task. Several questions are designed to cover the main aspects (e.g. Background, Method, Result, Conclusion) of the articles in natural sciences and medicine. A novel strategy is proposed to solve the problem of the non-existence of the ground truth answer spans. Then MRC-Sum is trained on the reconstructed datasets and large-scale pre-trained models. During the inference stage, four answer spans of the predefined questions are given by MRC-Sum and concatenated to form the final summary for each article. Experiments on three publicly available benchmarks, i.e., the Covid, PubMed, and arXiv datasets, demonstrate the effectiveness of MRC-Sum. Specifically, MRC-Sum outperforms advanced extractive summarization baselines on the Covid dataset and achieves competitive results on the PubMed and arXiv datasets. We also propose a novel metric, COMPREHS, to automatically evaluate the comprehensiveness of the system summaries for academic articles in natural sciences and medicine. Abundant experiments are conducted and verified the reliability of the proposed metric. And the results of the COMPREHS metric show that MRC-Sum is able to generate more comprehensive summaries than the baseline models.     

如何建设学习型团学联

广东金融学院保险系建系30年以来,一直秉承着与时俱进的原则,学生组织在老师们带领下,实现了从保险系团总支和学生会到保险系团总支学生会联合委员会的摸索。广东金融学院保险系和学生会联合委员会是在院团委领导和系党总支指导下开展工作的群众性组织,是党总支与学生之间相互沟通的桥梁和纽带,秉承着“诚信、团结、执着、创新”的保险系精神,以“自我管理、自我教育、自我服务”为宗旨,以锻炼及培养人才为目标的学生团体。     

高原或低氧环境下脱水与补液的研究进展

高原或低氧环境下体液平衡的改变主要表现为脱水和尿钠排泄。这与低氧刺激下交感神经兴奋性提高,外周化学感受器激发肾脏反应及体液调节激素的改变有关。低氧刺激使机体对营养素和液体的要求提高,但摄入降低,因此,机体应当补充足够的营养素和富含碳水化合物及电解质的液体。综述了高原或低氧环境下机体的体液平衡改变及其机制,提出了低氧下补液的主要原则,并指出了亟待研究的主要问题。     

中华人民共和国第10届运动会双人花样滑冰比赛综述

采用文献资料调研，现场观察和录相统计方法，对全国第10届运动会双人花样滑冰比赛进行分析，提出我国双人花样滑冰目前所处的位置和存在的不足，为我国花样滑冰项目在2006年第20届冬奥会上取得好的成绩，提供参考和建议。     

基于OBE理念的“生物医学工程”教学模式研究

该文认为,坚持以成果产出为导向,构建新型教学模式,对于培养高层次应用型生物医学工程人才十分必要。基于OBE理念重新定义生物医学工程人才培养目标,并据此反向构建应用型人才培养课程体系,开发丰富多样的教学手段,建立过程性考核和结果性考核相结合的综合考评体系,对于提高生物医学工程高层次应用人才培养质量、促进国家新产业新经济发展具有重大意义。     

网络论坛情报收集系统研究

本文提出一种基于网络论坛FAQ（Frequently Asked Questions）生成的情报收集系统的结构．并讨论了研发系统涉及的关键技术。在此基础上设计了系统性能评估实验。实验结果表明，研究的网络论坛情报收集系统能够有效地实现情报收集工作。