共查询到19条相似文献，搜索用时 156 毫秒
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在数字图书馆环境下,作者名歧义现象会降低文献数据库检索的准确性,影响文献数据集质量,自动化消歧方法相比于传统的方法将更有效地解决海量数据增长、人工辨识效率偏低的矛盾。在简述现有的具有代表性的作者名自动消歧方法的基础上,根据聚类方式和特征选取方式的不同,为其建立起一个较为完整的分类体系,并对其进行对比分析。然后针对文献数据库中存在的国内外作者名歧义现象,提出相应的不受限于某种数据库和语种的通用的人名消歧框架,从而为指导文献数据库系统如何应用合适的消歧方法提供技术支持。 相似文献
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【目的/意义】本文利用多源数据,通过对科技文献作者的名称进行消歧,使作者与科技文献呈一一对应的
关系。【方法/过程】本文提出首先将采集的多源数据进行预处理,形成了同一姓名作者文献组成的待消解的重名数
据集,通过合作关系构建学术圈以发现歧义,最后通过机构和领域进行消歧。【结果/结论】实验采集了各级教育、自
动化及计算机技术、信息与知识传播、数理科学和化学、无线电电子学、中国医学等6个不同的学科的文献题录数
据,本文提出的基于规则的消歧具有良好的消歧效果。通过多源数据融合、机构和领域多指标消歧,能够达到较高
的消歧效果。【创新/局限】解决了同机构同领域消歧的难题,并考虑了增量问题,构建了完整的消歧模型。 相似文献
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本文针对传统的聚类算法在入侵检测系统中的不足,提出一种基于密度的初始聚类中心的选择方法,可克服普通K-Means中的需人工确定K值的问题,用此算法改进的入侵检测模型能够获得很好的聚类效果。对比实验结果,发现使用改进后的算法与传统的K-Means相比可以获得更高的检测率和较低的误报率。 相似文献
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综合用户背景与资源特点,基于用户的协同过滤更适合高校图书馆在信息推荐中的应用。对其由于馆藏数字资源空间增大而导致推荐系统性能下降以及数据稀疏性问题,提出一种用户意图聚类的方法。通过运用K—means算法,对资源类别的意图特征值相似用户进行聚类,来提高推荐的实时性,降低数据稀疏性对信息推荐造成的影响。实验结果表明,基于用户意图聚类的协同过滤算法能有效提高推荐质量。 相似文献
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一种改进的K-means算法 总被引：1，自引：0，他引：1
K-means算法是聚类算法中最经典的划分算法之一,它对初值的依赖性很强,聚类结果随初始聚类中心选择的不同而波动很大。提出了一种改进的K-means算法,运用Kruskal算法生成聚类对象的最小生成树(MST),按权值从大到小删去K-1条边,得到的K个连通子图中对象的均值作为初始聚类中心进行聚类。由仿真实验表明,K-means算法较传统算法有更好的聚类效果和准确性。 相似文献
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聚类分析在图书馆馆藏书目中的挖掘与应用 总被引：1，自引：0，他引：1
苟元琴 《内蒙古科技与经济》2009,(13):140-141
文章从图书馆管理系统中引入数据挖掘技术,利用聚类分析中的K—means算法对图书馆馆藏图书借阅使用情况进行了聚类挖掘,并将挖掘结果进行分析,从而制定出相应的决策,以有针对性地丰富馆藏资源和优化图书馆的馆藏布局。 相似文献
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词义消歧是自然语言处理中的一个核心问题,尝试了基于单纯贝叶新概率模型的消歧方法,取得了好的效果。由于该方法在抽取上下文特征时没有进行合理的选择,致使一些无用的信息混入其中降低了贝叶斯分类器的分类准确率。利用词根词性提高了上下文特征抽取的有效性,并且尝试寻找上下文中的指示词这种特征进行消歧。 相似文献
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【目的/意义】为了实现高质量的数据清洗目标以提高专利大数据的利用效率，发明人姓名消歧成为了目前 一个亟待解决的关键性问题。【方法/过程】本文提出了基于专家研讨思想的发明人姓名消歧算法，即首先根据综合 相似度阈值将消歧过程中产生的发明人姓名歧义分为确定性歧义和非确定性歧义；然后对确定性歧义直接修正， 同时，引入专家研讨思想，通过群体智慧将非确定性歧义转化为确定性歧义进行消歧。【结果/结论】以国内医药行 业专利数据为实例的分析表明，与以往单纯的机器消歧算法相比，该消歧算法从准确率和消歧时间两个维度均具 有显著改进。 相似文献
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词义消歧是自然语言处理中的一个核心问题，尝试了基于单纯贝叶斯概率模型的消歧方法，取得了好的效果。由于该方法在抽取上下文特征时没有进行合理的选择，致使一些无用的信息混入其中降低了贝叶斯分类器的分类准确率。利用词根词性提高了上下文特征抽取的有效性，并且尝试寻找上下文中的指示词这种特征进行消歧。 相似文献
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Adrian-Gabriel Chifu Florentina Hristea Josiane Mothe Marius Popescu 《Information processing & management》2015
Word sense ambiguity has been identified as a cause of poor precision in information retrieval (IR) systems. Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query. However, the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generally supervised ones. In this paper we propose a new unsupervised method that uses word sense discrimination in IR. The method we develop is based on spectral clustering and reorders an initially retrieved document list by boosting documents that are semantically similar to the target query. For several TREC ad hoc collections we show that our method is useful in the case of queries which contain ambiguous terms. We are interested in improving the level of precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30) respectively. We show that precision can be improved by 8% above current state-of-the-art baselines. We also focus on poor performing queries. 相似文献
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Dictionary-based query translation for cross-language information retrieval often yields various translation candidates having different meanings for a source term in the query. This paper examines methods for solving the ambiguity of translations based on only the target document collections. First, we discuss two kinds of disambiguation technique: (1) one is a method using term co-occurrence statistics in the collection, and (2) a technique based on pseudo-relevance feedback. Next, these techniques are empirically compared using the CLEF 2003 test collection for German to Italian bilingual searches, which are executed by using English language as a pivot. The experiments showed that a variation of term co-occurrence based techniques, in which the best sequence algorithm for selecting translations is used with the Cosine coefficient, is dominant, and that the PRF method shows comparable high search performance, although statistical tests did not sufficiently support these conclusions. Furthermore, we repeat the same experiments for the case of French to Italian (pivot) and English to Italian (non-pivot) searches on the same CLEF 2003 test collection in order to verity our findings. Again, similar results were observed except that the Dice coefficient outperforms slightly the Cosine coefficient in the case of disambiguation based on term co-occurrence for English to Italian searches. 相似文献
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In this paper, we propose a new algorithm, which incorporates the relationships of concept-based thesauri into the document categorization using the k-NN classifier (k-NN). k-NN is one of the most popular document categorization methods because it shows relatively good performance in spite of its simplicity. However, it significantly degrades precision when ambiguity arises, i.e., when there exist more than one candidate category to which a document can be assigned. To remedy the drawback, we employ concept-based thesauri in the categorization. Employing the thesaurus entails structuring categories into hierarchies, since their structure needs to be conformed to that of the thesaurus for capturing relationships between categories. By referencing various relationships in the thesaurus corresponding to the structured categories, k-NN can be prominently improved, removing the ambiguity. In this paper, we first perform the document categorization by using k-NN and then employ the relationships to reduce the ambiguity. Experimental results show that this method improves the precision of k-NN up to 13.86% without compromising its recall. 相似文献
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Adriano Veloso Anderson A. FerreiraMarcos André Gonçalves Alberto H.F. LaenderWagner Meira Jr. 《Information processing & management》2012
Authorship disambiguation is an urgent issue that affects the quality of digital library services and for which supervised solutions have been proposed, delivering state-of-the-art effectiveness. However, particular challenges such as the prohibitive cost of labeling vast amounts of examples (there are many ambiguous authors), the huge hypothesis space (there are several features and authors from which many different disambiguation functions may be derived), and the skewed author popularity distribution (few authors are very prolific, while most appear in only few citations), may prevent the full potential of such techniques. In this article, we introduce an associative author name disambiguation approach that identifies authorship by extracting, from training examples, rules associating citation features (e.g., coauthor names, work title, publication venue) to specific authors. As our main contribution we propose three associative author name disambiguators: (1) EAND (Eager Associative Name Disambiguation), our basic method that explores association rules for name disambiguation; (2) LAND (Lazy Associative Name Disambiguation), that extracts rules on a demand-driven basis at disambiguation time, reducing the hypothesis space by focusing on examples that are most suitable for the task; and (3) SLAND (Self-Training LAND), that extends LAND with self-training capabilities, thus drastically reducing the amount of examples required for building effective disambiguation functions, besides being able to detect novel/unseen authors in the test set. Experiments demonstrate that all our disambigutators are effective and that, in particular, SLAND is able to outperform state-of-the-art supervised disambiguators, providing gains that range from 12% to more than 400%, being extremely effective and practical. 相似文献
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In-Su Kang Seung-Hoon Na Seungwoo Lee Hanmin Jung Pyung Kim Won-Kyung Sung Jong-Hyeok Lee 《Information processing & management》2009
Author name disambiguation deals with clustering the same-name authors into different individuals. To attack the problem, many studies have employed a variety of disambiguation features such as coauthors, titles of papers/publications, topics of articles, emails/affiliations, etc. Among these, co-authorship is the most easily accessible and influential, since inter-person acquaintances represented by co-authorship could discriminate the identities of authors more clearly than other features. This study attempts to explore the net effects of co-authorship on author clustering in bibliographic data. First, to handle the shortage of explicit coauthors listed in known citations, a web-assisted technique of acquiring implicit coauthors of the target author to be disambiguated is proposed. Then, a coauthor disambiguation hypothesis that the identity of an author can be determined by his/her coauthors is examined and confirmed through a variety of author disambiguation experiments. 相似文献
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Guan-Cheng Li Ronald Lai Alexander D’Amour David M. Doolin Ye Sun Vetle I. Torvik Amy Z. Yu Lee Fleming 《Research Policy》2014
Research into invention, innovation policy, and technology strategy can greatly benefit from an accurate understanding of inventor careers. The United States Patent and Trademark Office does not provide unique inventor identifiers, however, making large-scale studies challenging. Many scholars of innovation have implemented ad-hoc disambiguation methods based on string similarity thresholds and string comparison matching; such methods have been shown to be vulnerable to a number of problems that can adversely affect research results. The authors address this issue contributing (1) an application of the Author-ity disambiguation approach (0170 and 0175) to the US utility patent database, (2) a new iterative blocking scheme that expands the match space of this algorithm while maintaining scalability, (3) a public posting of the algorithm and code, and (4) a public posting of the results of the algorithm in the form of a database of inventors and their associated patents. The paper provides an overview of the disambiguation method, assesses its accuracy, and calculates network measures based on co-authorship and collaboration variables. It illustrates the potential for large-scale innovation studies across time and space with visualizations of inventor mobility across the United States. The complete input and results data from the original disambiguation are available at (http://dvn.iq.harvard.edu/dvn/dv/patent); revised data described here are at (http://funglab.berkeley.edu/pub/disamb_no_postpolishing.csv); original and revised code is available at (https://github.com/funginstitute/disambiguator); visualizations of inventor mobility are at (http://funglab.berkeley.edu/mobility/). 相似文献
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《Information processing & management》2005,41(4):1003-1018
The authors introduce an information visualization model, WebStar, for hyperlink-based information systems. Hyperlinks within a hyperlink-based document can be visualized in a two-dimensional visual space. All links are projected within a display sphere in the visual space. The relationship between a specified central document and its hyperlinked documents is visually presented in the visual space. In addition, users are able to define a group of subjects and to observe relevance between each subject and all hyperlinked documents via movement of that subject around the display sphere center. WebStar allows users to dynamically change an interest center during navigation. A retrieval mechanism is developed to control retrieved results in the visual space. Impact of movement of a subject on the visual document distribution is analyzed. An ambiguity problem caused by projection is discussed. Potential applications of this visualization model in information retrieval are included. Future research directions on the topic are addressed. 相似文献