共查询到19条相似文献，搜索用时 234 毫秒
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目前大多数自动标引方法不能有效利用文本中包含的多个特征。而支持向量机、条件随机场模型等统计机器学习模型能够有效利用文本包含的多种特征进行关键词提取。同时,由于各种自动标引模型性能各异,综合利用各种模型进行集成学习方式的自动标引,能够提高自动标引的质量。为了进一步提高自动标引的质量,本文试图整合统计机器学习模型与集成学习方法的优势,对文档进行基于多分类模型综合投票方式的自动标引。实验结果表明,基于集成学习方法的自动标引能提高标引结果的查准率和召回率。另外,集成学习标引模型中,基分类器加权的标引结果,优于基分类器未加权的标引结果。 相似文献
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基于条件随机场的自动标引模型研究 总被引：3，自引：1，他引：2
条件随机场(Conditional Random Fields,CRF)模型是一种概率图模型.为了有效利用标引对象的特征,并考虑到抽词标引可以转换为序列标注问题,本文提出基于条件随机场的自动抽词标引模型.实验结果表明,该模型在改善抽词标引的性能方面,要优于支持向量机、多元线性回归模型等其他机器学习方法,是到目前为止解决序列标注问题的最好方法.但是,该模型本身还不能解决由于样本中存在同义词和相近词带来的问题,需要进一步对训练集和标引过程中存在的词汇语义情况进行考虑,提高标引的质量. 相似文献
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基于字频向量的中文文本自动分类系统 总被引：15，自引：3，他引：12
本文提出了一种根据汉字统计特性和基于实例映射的中文文本自动分类方法。该方法采用汉字字频向量作为文本的表示方法。它的显著特点是引入线性最小二乘方估计技术建立文本分类器模型,通过对训练集语料的手工分类标引以及对文本和类别间的相关性判定的学习,实现了基于全局最小错误率的汉字一类别两个向量空间的映射函数,并用该函数对测试文本进行分类。 相似文献
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基于词典约简及多分类算法的文本分类系统的设计与开发 总被引：3，自引：1，他引：2
文本自动分类是目前机器学习、自然语言处理和信息资源检索领域的研究热点之一.本文在对自动分类的实现技术问题进行探讨的基础上,尝试对自动分类的几个环节提出改进措施.具体包括:采用约简法进行抽词词典的构造,采用投票法进行文本特征的选择,采用层次法进行逐层次的分类,采用统计与规则相结合的方法进行分类器的构造等.通过在不同语料库上与传统分类方法的对比测试表明,上述改进措施能够有效提高自动分类的性能,基于这些改进措施所开发的自动分类系统具有大规模文本自动分类的可行性.本文详细讨论了相关改进措施的原理、实现算法、流程及存在的问题. 相似文献
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[目的/意义] 论文摘要是信息组织的重要标引对象,将论文摘要按一定结构进行标引有利于科学传播、知识发现和情报分析。如何对现有非结构式摘要进行精准快速的自动标引是亟待解决的现实问题。[方法/过程] 假定不同类别的摘要具有内在一致性,即对结构式摘要的研究可为非结构式摘要自动标引提供方法和技术参考。据此,基于美国国家医学图书馆结构要素标签术语集和标签分类映射关系,提出结构要素BOMRC体系和结构式摘要的识别与规范化标引方法。其次选取研究样本并采用文本挖掘方法对样本语料中的单词、动词、三词词块、四词词块等词汇进行词频、TFIDF值等多个指标的定量统计分析,构建能够进行结构要素识别的语义特征词典。最后利用非结构式摘要测试集进行语义特征词典有效性检验。[结果/结论] 结果显示,利用语义特征词典方法能够有效识别非结构式摘要的各类要素,并可用于优化以机器学习方法为核心的自动识别模型。 相似文献
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[目的/意义]文章对科技政策隐性扩散路径自组织方法进行研究,挖掘科技政策文本包含深层语义信息,将隐性知识显性化,为科研人员拓展和丰富政策扩散路径研究提供参考。[方法/过程]本文结合科技政策篇章文本的形式语义和内容语义两个方面对政策文本结构化处理和深度挖掘,对政策文本资源全解析,抽取科技政策文本中包含的特征,其中包括概念和关系自动获取与标引技术、网络表示学习,挖掘科技政策文本中的隐含结构信息,利用BiLSTM-CRF模型的深度学习方法实现概念的自动获取和自动标引关系。将得到多篇科技政策文本的概念和关系组成概念关系对的形式,借助于表示学习的方法发现每个节点稠密的向量表示。[结果/结论]通过实验验证,证明了本文借助隐性路径特征的科技政策扩散隐性路径自组织方法的有效性,在一定程度上拓展了政策研究的方法,为科研人员在政策扩散研究上提供了参考。 相似文献
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互联网的蓬勃发展使得文本数据呈指数型增长态势,如何实现文本内容的高效分类成为信息资源管理工作面临的紧要问题。本文以维普学术期刊资源与百度新闻网页作为基础语料集,基于LDA模型抽取文档主题、切分文本内容,融合集成学习Catboost算法获得文档在主题上的概率分布,然后利用训练集提取出的隐含主题-文本矩阵进行分类器训练,最终构建文本分类系统。研究结果显示,该系统能够有效完成文本混合自动分类,分类误差率较低,分类性能明显优于传统的文本分类方法。 相似文献
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自动标引通用评价模型研究 总被引：1，自引：0，他引：1
目前大多文档都不具有关键词,但手工标引关键词费时费力且主观性较强,因此关键词自动标引是一项值得研究的技术,由此引发的标引结果有效评价问题也成为一个亟需解决的问题.然而,评估关键词自动标引的性能并非一件容易的事情.针对常规自动标引评价方法存在的评价结果不能完全反映真实的标引结果以及评价成本高的情况,本文提出一种通用的自动标引评价模型.该模型可以有效地利用外部资源,在有参照情况下与无参照情况下,分别对标引结果进行评价.实验结果表明,自动标引通用评价模型能增加标引评价的可靠性,并且降低标引评价的成本. 相似文献
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《Journal of Informetrics》2022,16(4):101349
Prediction of the future performance of academic journals is a task that can benefit a variety of stakeholders including editorial staff, publishers, indexing services, researchers, university administrators and granting agencies. Using historical data on journal performance, this can be framed as a machine learning regression problem. In this work, we study two such regression tasks: 1) prediction of the number of citations a journal will receive during the next calendar year, and 2) prediction of the Elsevier CiteScore a journal will be assigned for the next calendar year. To address these tasks, we first create a dataset of historical bibliometric data for journals indexed in Scopus. We propose the use of neural network models trained on our dataset to predict the future performance of journals. To this end, we perform feature selection and model configuration for a Multi-Layer Perceptron and a Long Short-Term Memory. Through experimental comparisons to heuristic prediction baselines and classical machine learning models, we demonstrate superior performance in our proposed models for the prediction of future citation and CiteScore values. 相似文献
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《图书馆管理杂志》2013,53(1-2):41-48
Abstract The primary goal of the ELIXIR project was to achieve improvements in the performance of text-retrieval systems by confronting some of the challenges presented by the indexing of full-text documents. We explored two hypotheses involving the use of linguistic analysis to improve the automatic creation of indexes: (1) if it is possible to develop algorithms that index only the words which most closely capture the document's content-not the complete text, as is done in existing sys-tems-the retrieval of irrelevant documents might be minimized; (2) if it is possible to create indexing algorithms that perform a minimal syntactic parse required to capture important phrases, the index may represent the document's content even more accurately, increasing the likelihood that the article can be retrieved by a relevant query. 相似文献
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关键词自动标引是一种识别有意义且具有代表性片段或词汇的自动化技术。关键词自动标引可以为自动摘要、自动分类、自动聚类、机器翻译等应用提供辅助作用。本文利用基于知网的词语语义相关度算法对词汇链的构建算法进行了改进,并结合词频和词的位置等统计信息,进行关键词的自动标引。实验证明,该方法可以有效的进行关键词的自动标引。 相似文献
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��[Purpose/significance] The abstract of scientific papers is a vital indexing object within information organization. Meanwhile, indexing the abstract according to certain rules is conducive for not only scientific communication or knowledge discovery, and intelligence analysis as well. Thus, how to realize auto-index accurately and quickly, for millions of unstructured abstracts existed nowadays is a crucial problem to be addressed.[Method/process] This study assumed that different categories of abstract are inherently consistent, that is, the study of structured abstract can provide a method and technical reference for unstructured abstract auto-indexing. Acting in accordance with this assumption and based on the US National Library of Medicine's structural element labeling terminology, this study accomplished mapping across abstract element classifications and proposed BOMRC system, a normalization indexing method for structured abstract. Then we collected research sample and used text mining method to analyze multiple features of structured abstract quantitatively and statistically, such as word frequency, TF-IDF value, as for dimension of words, verbs, three-word lexical chunks and four-word lexical chunks, which enabled us propose a semantic feature dictionary for structured elements. Finally, we used unstructured abstract to test the validity of the semantic feature dictionary.[Result/conclusion] The results show that the semantic feature dictionary method can effectively identify various structural elements of scientific paper abstract, and it can be used to optimize the automatic recognition model, which may be based on machine learning methods. 相似文献
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本文介绍了几种自动标引加权方法,以及把这几种方法应用于汉语文献自动标引试验的情况,给出了试验的有关数据、基于数据的分析和推出的结论,并对汉语科技文献自动标引中的若干问题进行了探讨。 相似文献
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A test of the performance of an automatic indexing procedure based on the processing of the full text of medical journal articles shows results comparable to those achieved using Excerpta Medica and MEDLARS manual indexing. A file of nineteen documents was partitioned by sixteen questions, for each of which two search strategies in each indexing language were formulated. The results were compared to ideal answers chosen by a fourth-year medical student who read the entire file and posed the questions. 相似文献
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Christoph Baumgarten 《Information Retrieval》2000,3(3):253-271
This paper describes a probabilistic model for optimum information retrieval in a distributed heterogeneous environment.The model assumes the collection of documents offered by the environment to be partitioned into subcollections. Documents as well as subcollections have to be indexed, where indexing methods using different indexing vocabularies can be employed. A query provided by a user is answered in terms of a ranked list of documents. The model determines a procedure for ranking the documents that stems from the Probability Ranking Principle: For each subcollection, the subcollection's documents are ranked; the resulting ranked lists are combined into a final ranked list of documents, where the ordering is determined by the documents' probabilities of being relevant with respect to the user's query. Various probabilistic ranking methods may be involved in the distributed ranking process. A criterion for effectively limiting the ranking process to a subset of subcollections extends the model.The property that different ranking methods and indexing vocabularies can be used is important when the subcollections are heterogeneous with respect to their content.The model's applicability is experimentally confirmed. When exploiting the degrees of freedom provided by the model, experiments showed evidence that the model even outperforms comparable models for the non-distributed case with respect to retrieval effectiveness. 相似文献