共查询到18条相似文献，搜索用时 140 毫秒
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[目的/意义]基于内容的过滤推荐中,针对向量空间模型表示文本时容易造成维度灾难的问题,提出利用余弦值r与匹配度值Sim相结合的方法对原有模型进行改进。[方法/过程]由文献资源和用户兴趣分别筛选出权重较大特征词的词向量,进而由公式计算余弦值r,结合对应的特征词权重进一步计算出匹配度值Sim,将其作为向目标用户推荐文献的依据,并利用河北工业大学图书馆的相关数据对改进模型、向量空间模型及LDA主题模型进行实验,最后利用查准率、召回率、F1值及运行时间等评价指标对3种模型的实验结果进行分析。[结果/结论]实验结果表明所提出的改进模型相比较于实验中的向量空间模型与LDA主题模型具有更高的应用价值与运行效率。 相似文献
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文本相似度的计算方法以采用TF-IDF的方法对文本建模成词频向量空间模型（VSM）为主,本文结合科技期刊文献和专利文献特点,对TF-IDF的计算方法进行了改进,将词频的统计改进为科技术语的频率统计,提出了一种针对科技文献相似度的计算方法,该方法首先应用自然语言处理技术对科技文献进行预处理,采用科技术语的自动抽取方法进行科技文献术语的自动抽取,结合该文提出的术语权重计算公式构建向量空间模型,来计算科技期刊文献和专利文献之间的相似度。并利用真实有效的科学期刊和文献数据进行实验测试,实验结果表明文中提出的方法优于传统的TF-IDF计算方法。 相似文献
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基于语义网计算英语词语相似度 总被引：14，自引：2，他引：14
本文介绍一种基于WordNet的计算英语词语相似度的实现方法:从WordNet中提取同义词并采取向量空间方法计算英语词语的相似度。向量包括三方面:(1)WordNet的同义词词集(Synset),(2)类属信息(Class),(3)意义解释(Sense explanation)。实验结果表明,这是计算英语词语相似度的一种可行的方法。 相似文献
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以矩阵理论作为研究的切入点,将经典向量空间模型中常用的向量和集合以矩阵的形式加以重构,并认为基于向量内积法的相似性计算与相应矩阵的乘法运算等价。结合稀疏矩阵和数据稀疏的定义,分析VSM信息检索背景下数据稀疏产生的原因;同时,讨论三种情形下数据稀疏对相似性计算的共同影响--部分毫无意义的时间复杂度。最后,给出规避数据稀疏问题的三层策略:文本级策略、文本集级策略和矩阵级策略。 相似文献
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基于概念向量空间的文档语义分类模型研究 总被引：1，自引：0，他引：1
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在实际应用中,许多研究对象都是抽象的,难以用某种特征向量的形式表示,这使得许多成熟的数据挖掘和机器学习方法难以被采用.不过,通常可将其转化成一个Proximity数据矩阵,使得矩阵中的元素表示两个对象间某种"比较"关系.针对该问题,本文提出仅根据Proximity数据矩阵利用多维尺度分析法(MDS)将研究对象进行向量化表示,即构建了一种向量空间模型.最后,对汉语科技词系统中的词语进行了聚类分析,结果表明,向量空间模型构建后再聚类的结果明显优于直接针对Proximity数据进行聚类分析的结果,从而验证了该方法的可行性和有效性. 相似文献
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The paper proposes a Vector Space Model over the Cayley-Klein Hyperbolic Geometry (referred to as Hyperbolic Information Retrieval = HIR) using a similarity measure derived from the hyperbolic distance. It is shown that the proposed model is equivalent with the classical Vector Space Model using Cosine measure with normalized weighting scheme. It is also shown that the categoricity of the new retrieval system can be varied by only modifying the radius of the hyperbolic space and without using a different weighting scheme and similarity measure, which is not the case in the VSM, where the same effect can only be obtained by both changing the weighting scheme and similarity measure at the expense of a more costly computation. Experiments are also reported to demonstrate and support the ideas, and they show that categoricity in HIR can be varied more than O(n) faster, where n is the number of index terms, than in the VSM. 相似文献
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This paper describes a method, using Genetic Programming, to automatically determine term weighting schemes for the vector
space model. Based on a set of queries and their human determined relevant documents, weighting schemes are evolved which
achieve a high average precision. In Information Retrieval (IR) systems, useful information for term weighting schemes is
available from the query, individual documents and the collection as a whole.
We evolve term weighting schemes in both local (within-document) and global (collection-wide) domains which interact with
each other correctly to achieve a high average precision. These weighting schemes are tested on well-known test collections
and are compared to the traditional tf-idf weighting scheme and to the BM25 weighting scheme using standard IR performance metrics.
Furthermore, we show that the global weighting schemes evolved on small collections also increase average precision on larger
TREC data. These global weighting schemes are shown to adhere to Luhn’s resolving power as both high and low frequency terms
are assigned low weights. However, the local weightings evolved on small collections do not perform as well on large collections.
We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on
large collections. 相似文献
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《Journal of Informetrics》2020,14(4):101076
The effective representation of the relationship between the documents and their contents is crucial to increase classification performance of text documents in the text classification. Term weighting is a preprocess aiming to represent text documents better in Vector Space by assigning proper weights to terms. Since the calculation of the appropriate weight values directly affects performance of the text classification, in the literature, term weighting is still one of the important sub-research areas of text classification. In this study, we propose a novel term weighting (MONO) strategy which can use the non-occurrence information of terms more effectively than existing term weighting approaches in the literature. The proposed weighting strategy also performs intra-class document scaling to supply better representations of distinguishing capabilities of terms occurring in the different quantity of documents in the same quantity of class. Based on the MONO weighting strategy, two novel supervised term weighting schemes called TF-MONO and SRTF-MONO were proposed for text classification. The proposed schemes were tested with two different classifiers such as SVM and KNN on 3 different datasets named Reuters-21578, 20-Newsgroups, and WebKB. The classification performances of the proposed schemes were compared with 5 different existing term weighting schemes in the literature named TF-IDF, TF-IDF-ICF, TF-RF, TF-IDF-ICSDF, and TF-IGM. The results obtained from 7 different schemes show that SRTF-MONO generally outperformed other schemes for all three datasets. Moreover, TF-MONO has promised both Micro-F1 and Macro-F1 results compared to other five benchmark term weighting methods especially on the Reuters-21578 and 20-Newsgroups datasets. 相似文献
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In this article, we introduce an out-of-the-box automatic term weighting method for information retrieval. The method is based on measuring the degree of divergence from independence of terms from documents in terms of their frequency of occurrence. Divergence from independence has a well-establish underling statistical theory. It provides a plain, mathematically tractable, and nonparametric way of term weighting, and even more it requires no term frequency normalization. Besides its sound theoretical background, the results of the experiments performed on TREC test collections show that its performance is comparable to that of the state-of-the-art term weighting methods in general. It is a simple but powerful baseline alternative to the state-of-the-art methods with its theoretical and practical aspects. 相似文献
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一种用于主题提取的非线性加权方法 总被引：15，自引：0，他引：15
主题提取是文本处理的一项重要工作。本文首先分析了主题抽取中加权方法形成时的一些定量问题,然后提出了主题相关词一种非线性加权处理方法,对比实验结果显示它不仅是一种比较稳健的方法,而且能在一定程度上提高主题提取的正确率。 相似文献
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对信息检索系统返回结果相关度的改进,一直是信息检索领域重要的研究内容。本文首先引入查询词出现信息的概念,随后给出了查询词出现权重的形式化表示,进而将其与BM25模型结合起来。对于查询词出现权重的计算,本文采用了两种方法,即线性加权方法和因数加权方法。我们通过在GOV2数据集上的实验发现,无论哪种方法,通过加入查询词出现权重,都可以有效的改进检索结果的相关度。实验显示,对于TREC 2005的查询,MAP值的改进达到15.78%,p@10的改进达到3468%。本文所描述的方法已经应用到TREC 2009的WebTrack中。 相似文献
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We investigate the effect of feature weighting on document clustering, including a novel investigation of Okapi BM25 feature
weighting. Using eight document datasets and 17 well-established clustering algorithms we show that the benefit of tf-idf weighting over tf weighting is heavily dependent on both the dataset being clustered and the algorithm used. In addition, binary weighting
is shown to be consistently inferior to both tf-idf weighting and tf weighting. We investigate clustering using both BM25 term saturation in isolation and BM25 term saturation with idf, confirming that both are superior to their non-BM25 counterparts under several common clustering quality measures. Finally,
we investigate estimation of the k1 BM25 parameter when clustering. Our results indicate that typical values of k1 from other IR tasks are not appropriate for clustering; k1 needs to be higher. 相似文献
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针对目前信息服务机构只能提供文献的检索服务而不提供表格检索功能这一现状,提出一种基于向量空间模型的表格检索算法,并从表格特征抽取、特征词权值设置、检索结果匹配排序等方面进行讨论,为未来表格检索服务提供一定的理论依据。 相似文献