共查询到18条相似文献，搜索用时 296 毫秒
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一种基于TFIDF方法的中文关键词抽取算法 总被引：4，自引：1，他引：3
本文在海量智能分词基础之上,提出了一种基于向量空间模型和TFIDF方法的中文关键词抽取算法.该算法在对文本进行自动分词后,用TFIDF方法对文献空间中的每个词进行权重计算,然后根据计算结果抽取出科技文献的关键词.通过自编软件进行的实验测试表明该算法对中文科技文献的关键词自动抽取成效显著. 相似文献
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[研究目的]为了帮助政府、企业和科研人员从海量的听证公开文本中发现科技相关政策和热点,快速、全面地识别出有价值的信息。[研究方法]梳理听证公开文本的类型与特点,并对其中有价值的信息进行合理的界定与分类;根据文本的内容特征和话语特征提出事件句识别、事件类型检测和事件论元抽取的三阶段式事件抽取方法,以实现有价值信息的抽取;对抽取的有价值信息进行深入分析。[研究结论]与基准模型相比,该研究所提方法在事件句识别召回率上提高33%,F1提高17%,在事件类型检测的精确率上提高1%,在事件论元抽取的精确率上提高18%,召回率提高4%,取得了一定效果,为此类文本进一步分析提供了新研究思路。 相似文献
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[目的/意义]近年来,科技文献资源呈爆炸性增长,海量科技文献中依旧存在大量非结构化摘要。非结构化摘要一方面不利于学者阅读与理解;另一方面不利于对摘要内部信息进行知识的自动化抽取和相应的检索。研究科技文献非结构化摘要的知识表示模型及其自动化抽取方法,对学者快速阅读和机器自动化处理具有重要意义。[方法/过程]文章在分析科技文献非结构化摘要结构的基础上,结合知识元本体理论,构建了一个面向科技文献非结构化摘要的知识元本体模型。通过分析非结构化摘要的写作特征,将文本按句子级划分为目的、方法、结果或结论三个要素,统计每个要素句中的线索词、句型和位置,建立相关规则库,根据本体模型和规则库构建相关抽取算法。最后,下载《计算机技术与发展》中的部分文献进行实验。[结果/结论]通过增加句型集和线索词集,完善了非结构化摘要的要素,构建了非结构化摘要知识元本体模型。实验结果表明,根据本文提出的模型能有效地对非结构化摘要中的知识元进行抽取。[局限]实验的不足之处是需要人工对摘要中的句型和线索词进行归纳总结。 相似文献
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作为信息抽取的重要组成部分,事件抽取的研究具有广阔的应用前景和巨大的现实意义.文章阐述事件及事件抽取的基本概念,简单介绍事件抽取的发展历史,并从元事件抽取和主题事件抽取两个方面出发,详细介绍主要的研究方法与研究现状,总结事件抽取发展所面临的挑战,并在此基础上展望事件抽取的研究趋势. 相似文献
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基于模式匹配抽取技术的网上产品情报获取 总被引：1，自引：0，他引：1
产品生命周期的逐渐缩短迫使企业关注从因特网上大量、散乱的信息中及时获取新产品信息,跟踪竞争对手研发动向。本文引入基于模式匹配的自动Web信息抽取技术,叙述抽取产品的关键信息方法,并以家用冰箱性能参数信息的自动抽取为例,分析了冰箱领域知识,进行了样本页面的分析归纳,确认了冰箱产品的多种属性及产品信息抽取的模式特征,最终获得了清晰、结构化的产品数据,形成从Web页面上抽取同类产品关键信息的整个处理流程模型,成为网络环境下情报采集与分析的新情报研究模式的一个有力探索。 相似文献
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[目的/意义]研究利用深度学习的循环神经网络GRU结合条件随机场CRF对标注的中文文本序列进行预测,来抽取在线评论文本中的实体-属性。[方法/过程]首先根据设计好的文本序列标注规范,对评论语料分词后进行实体及其属性的命名实体标注,得到单词序列、词性序列和标注序列;然后将单词序列、词性序列转为分布式词向量表示并用于GRU循环神经网络的输入;最后输出层采用条件随机场CRF,输出标签即是实体或属性。[结果/结论]实验结果表明,本文的方法将实体-属性抽取简化为命名实体标注,并利用深度学习的GRU捕获输入数据的上下文语义以及条件随机场CRF获取输出标签的前后关系,比传统的基于规则或一般的机器学习方法具有较大的应用优势。 相似文献
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[研究目的]为细粒度发现相近学科间的知识关联,推动学科间知识交流,通过抽取方法知识元,探析不同学科领域研究方法的交流态势。[研究方法]以情报学和计算机科学学为例,首先,编写方法知识元抽取规则,基于规则抽取两学科领域大样本文献的方法知识元,构建学科领域方法知识元库;再利用LDA模型抽取两学科领域代表性期刊的方法知识元;以此为基础,对比分析两学科领域的高频研究方法、已交流的研究方法、存在交流潜力的研究方法。[研究结论]研究发现,两学科已交流的研究方法有：聚类分析、LDA模型、BP神经网络、支持向量机、主成分分析、协同过滤、文献计量等;具有交流潜力的研究方法有：提高准确率的分类算法(AdaBoost算法、XGBoost算法、SMOTE算法)、图卷积神经网络、自然语言处理模型GloVe模型等。该研究不仅能为情报学研究者选择计算机科学学领域的研究方法提供参考,而且也为学科交叉研究提供了一种新思路。 相似文献
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[目的/意义]互联网开源信息具有海量、多源异构等特点,如何从中及时发现有价值的信息,并对信息加以情报利用一直是情报研究和实践应用的重点领域之一。文章提出一套开源的学术会议领域信息抽取框架,解决信息抽取语义理解和关联融合的问题,并将其应用于情报研究中,为解决情报分析的智能应用提供工具和方法。[方法/过程]采集网络上新闻报道的内容,采用预训练语言模型方法挖掘学术会议事件信息,提出面向特定领域的信息抽取框架,实现会议名称及其举办时间、地点、出席人、发言人信息的抽取,并进行实际的情报利用研究案例分析。[结果/结论]相较于单一抽取实体的方法,抽取会议核心元素更具有情报关联性和准确性,可以更好地对开源热点事件发现和跟踪监测、目标人物轨迹动向分析等研究提供方法,为情报分析应用和情报研判奠定基础。 相似文献
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Within the context of Information Extraction (IE), relation extraction is oriented towards identifying a variety of relation phrases and their arguments in arbitrary sentences. In this paper, we present a clause-based framework for information extraction in textual documents. Our framework focuses on two important challenges in information extraction: 1) Open Information Extraction and (OIE), and 2) Relation Extraction (RE). In the plethora of research that focus on the use of syntactic and dependency parsing for the purposes of detecting relations, there has been increasing evidence of incoherent and uninformative extractions. The extracted relations may even be erroneous at times and fail to provide a meaningful interpretation. In our work, we use the English clause structure and clause types in an effort to generate propositions that can be deemed as extractable relations. Moreover, we propose refinements to the grammatical structure of syntactic and dependency parsing that help reduce the number of incoherent and uninformative extractions from clauses. In our experiments both in the open information extraction and relation extraction domains, we carefully evaluate our system on various benchmark datasets and compare the performance of our work against existing state-of-the-art information extraction systems. Our work shows improved performance compared to the state-of-the-art techniques. 相似文献
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[目的/意义]移动互联时代,以微信为代表的自媒体开启了一种新型的商业化自媒体信息服务模式,国家对自媒体信息服务产业的管理也由宽松逐步进入制度规范时期。[方法/过程]运用历史研究法、文献分析法对我国自媒体信息服务立法演化历程进行分期,分析了不同阶段的立法背景与演化路径、对立法内容进行分析与总体评价。[结果/结论]随着自媒体信息服务法律法规的精确化、制度化,我国自媒体信息服务进入有序、健康的良性发展阶段,自媒体信息服务法律制度建设也将进入不断完善时期。 相似文献
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Hao Zhang Frank Boons Riza Batista-Navarro 《Information processing & management》2019,56(5):1837-1848
Narratives are comprised of stories that provide insight into social processes. To facilitate the analysis of narratives in a more efficient manner, natural language processing (NLP) methods have been employed in order to automatically extract information from textual sources, e.g., newspaper articles. Existing work on automatic narrative extraction, however, has ignored the nested character of narratives. In this work, we argue that a narrative may contain multiple accounts given by different actors. Each individual account provides insight into the beliefs and desires underpinning an actor’s actions. We present a pipeline for automatically extracting accounts, consisting of NLP methods for: (1) named entity recognition, (2) event extraction, and (3) attribution extraction. Machine learning-based models for named entity recognition were trained based on a state-of-the-art neural network architecture for sequence labelling. For event extraction, we developed a hybrid approach combining the use of semantic role labelling tools, the FrameNet repository of semantic frames, and a lexicon of event nouns. Meanwhile, attribution extraction was addressed with the aid of a dependency parser and Levin’s verb classes. To facilitate the development and evaluation of these methods, we constructed a new corpus of news articles, in which named entities, events and attributions have been manually marked up following a novel annotation scheme that covers over 20 event types relating to socio-economic phenomena. Evaluation results show that relative to a baseline method underpinned solely by semantic role labelling tools, our event extraction approach optimises recall by 12.22–14.20 percentage points (reaching as high as 92.60% on one data set). Meanwhile, the use of Levin’s verb classes in attribution extraction obtains optimal performance in terms of F-score, outperforming a baseline method by 7.64–11.96 percentage points. Our proposed approach was applied on news articles focused on industrial regeneration cases. This facilitated the generation of accounts of events that are attributed to specific actors. 相似文献
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In this paper, we address the problem of relation extraction of multiple arguments where the relation of entities is framed by multiple attributes. Such complex relations are successfully extracted using a syntactic tree-based pattern matching method. While induced subtree patterns are typically used to model the relations of multiple entities, we argue that hard pattern matching between a pattern database and instance trees cannot allow us to examine similar tree structures. Thus, we explore a tree alignment-based soft pattern matching approach to improve the coverage of induced patterns. Our pattern learning algorithm iteratively searches the most influential dependency tree patterns as well as a control parameter for each pattern. The resulting method outperforms two baselines, a pairwise approach with the tree-kernel support vector machine and a hard pattern matching method, on two standard datasets for a complex relation extraction task. 相似文献
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通过对农业网页的HTML结构和特征研究,叙述基于文本内容的农业网页信息抽取和分类实验研究过程。实验中利用DOM结构对农业网页信息进行信息抽取和预处理,并根据文本的内容自动计算文本类别属性,得到特征词,通过总结样本文档的特征,对遇到的新文档进行自动分类。实验结果表明,本文信息提取的时间复杂度比较小、精确度高,提高了分类的正确率。 相似文献
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《Information processing & management》2023,60(4):103153
Few-Shot Event Classification (FSEC) aims at assigning event labels to unlabeled sentences when limited annotated samples are available. Existing works mainly focus on using meta-learning to overcome the low-resource problem that still requires abundant held-out classes for model learning and selection. Thus we propose to deal with the low-resource problem by utilizing prompts. Further, existing methods suffer from severe trigger biases that may result in ignorance of the context. That is, the correct classifications are gained by looking at only the triggers, which hurts the model’s generalization ability. Thus, we propose a knowledgeable augmented-trigger prompt FSEC framework (AugPrompt), which can overcome the bias issues and alleviates the classification bottleneck brought by insufficient data. In detail, we first design an External Knowledge Injection (EKI) module to incorporate an external knowledge base (Related Words) for trigger augmentation. Then, we propose an Event Prompt Generation (EPG) module to generate appropriate discrete prompts for initializing the continuous prompts. After that, we propose an Event Prompt Tuning (EPT) module to automatically search prompts in the continuous space for FSEC and finally predict the corresponding event types of the inputs. We conduct extensive experiments on two public English datasets for FSEC, i.e., FewEvent and RAMS. The experimental results show the superiority of our proposal over the competitive baselines, where the maximum accuracy increase compared to the strongest baseline reaches 10.8%. 相似文献