基于改进卡方统计量的藏文文本表示方法

藏文文本表示是将非结构化的藏文文本转换为计算机能够处理的数据形式,是藏文文本分类、文本聚类等领域特征抽取的前提。传统的藏文文本表示方法较少考虑特征项之间的关联度,容易造成语义损失。为此,结合向量空间模型,提出一种新的藏文文本表示方法。提取文本中词频统计TF-IDF值较高的部分词项作为对比词项,对藏文文本进行断句处理,以每个句子作为一个语境主题,利用卡方统计量计算文本中词项与对比词项的关联程度。实验结果表明,与传统的向量空间模型相比,该方法能更准确地表示藏文文本。     

Statistical semantics for enhancing document clustering

Document clustering algorithms usually use vector space model (VSM) as their underlying model for document representation. VSM assumes that terms are independent and accordingly ignores any semantic relations between them. This results in mapping documents to a space where the proximity between document vectors does not reflect their true semantic similarity. This paper proposes new models for document representation that capture semantic similarity between documents based on measures of correlations between their terms. The paper uses the proposed models to enhance the effectiveness of different algorithms for document clustering. The proposed representation models define a corpus-specific semantic similarity by estimating measures of term–term correlations from the documents to be clustered. The corpus of documents accordingly defines a context in which semantic similarity is calculated. Experiments have been conducted on thirteen benchmark data sets to empirically evaluate the effectiveness of the proposed models and compare them to VSM and other well-known models for capturing semantic similarity.     

基于概念全信息空间的文本知识挖掘算法

提出了用非负矩阵因子分解法(NMF)得到的基向量作为概念空间,并用其代替原特征向量空间来表示文本特征的方法,该方法不仅降低了特征空间的维数而且弥补了传统表示法无法体现特征相关性的不足.在此基础上,提出了基于概念全信息空间的文本知识挖掘算法.实验结果表明,与单纯的基于语义的知识挖掘方法相比,提出的算法具有更好的精度与效率,提取的知识既包含具体内容又体现知识的价值,能够为智能决策的合理性提供可靠的依据.     

基于高性能特征选择函数的Web文档聚类算法*

目前常用向量空间模型 VSM(vector space model)表示文档,造成的高维问题制约了其实际应用的效果。采用了一种高性能特征选择函数,在构建VSM时选取对区分类别贡献较大的特征词,因此有效地降低了特征空间的纬度,大大提高了系统的效率,改善了聚类的效果。通过真实数据集上的实验,证明其性能优于传统方法。     

Knowledge-based vector space model for text clustering

This paper presents a new knowledge-based vector space model (VSM) for text clustering. In the new model, semantic relationships between terms (e.g., words or concepts) are included in representing text documents as a set of vectors. The idea is to calculate the dissimilarity between two documents more effectively so that text clustering results can be enhanced. In this paper, the semantic relationship between two terms is defined by the similarity of the two terms. Such similarity is used to re-weight term frequency in the VSM. We consider and study two different similarity measures for computing the semantic relationship between two terms based on two different approaches. The first approach is based on the existing ontologies like WordNet and MeSH. We define a new similarity measure that combines the edge-counting technique, the average distance and the position weighting method to compute the similarity of two terms from an ontology hierarchy. The second approach is to make use of text corpora to construct the relationships between terms and then calculate their semantic similarities. Three clustering algorithms, bisecting k-means, feature weighting k-means and a hierarchical clustering algorithm, have been used to cluster real-world text data represented in the new knowledge-based VSM. The experimental results show that the clustering performance based on the new model was much better than that based on the traditional term-based VSM.     

一种结合上下文语义的短文本聚类算法

短文本因具有特征信息不足且高维稀疏等特点,使得传统文本聚类算法应用于短文本聚类任务时性能有限。针对上述情况,提出一种结合上下文语义的短文本聚类算法。首先借鉴社会网络分析领域的中心性和权威性思想设计了一种结合上下文语义的特征词权重计算方法,在此基础上构建词条-文本矩阵;然后对该矩阵进行奇异值分解,进一步将原始特征词空间映射到低维的潜在语义空间;最后通过改进的K-means聚类算法在低维潜在语义空间完成短文本聚类。实验结果表明,与传统的基于词频及逆向文档频权重的文本聚类算法相比,该算法能有效改善短文本特征不足及高维稀疏性,提高了短文的本聚类效果。     

基于语义特征空间上下文的短文本表示学习

文本表示是自然语言处理中的基础任务,针对传统短文本表示高维稀疏问题,提出1种基于语义特征空间上下文的短文本表示学习方法。考虑到初始特征空间维度过高,通过计算词项间互信息与共现关系,得到初始相似度并对词项进行聚类,利用聚类中心表示降维后的语义特征空间。然后,在聚类后形成的簇上结合词项的上下文信息,设计3种相似度计算方法分别计算待表示文本中词项与特征空间中特征词的相似度,以形成文本映射矩阵对短文本进行表示学习。实验结果表明,所提出的方法能很好地反映短文本的语义信息,能对短文本进行合理而有效的表示学习。     

基于WordNet概念向量空间模型的文本分类

文章提出了一种文本特征提取方法,以WordNet语言本体库为基础,以同义词集合概念代替词条,同时考虑同义词集合间的上下位关系,建立文本的概念向量空间模型作为文本特征向量,使得在训练过程中能够提取出代表类别的高层次信息。实验结果表明,当训练文本集合很小时,方法能够较大地提高文本的分类准确率。     

Automatic classification of Tamil documents using vector space model and artificial neural network

Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), K-nearest neighbor (KNN), Naives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of VSM and ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the VSM and ANN models. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word-matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% which is better than the performance of VSM which yields 90.33% on Tamil document classification.     

基于语义的VSM模型改进

文本聚类在很多领域都有广泛的应用,传统的文本聚类方法由于并不考虑语义因素,得出的聚类效果并不理想.利用语义对VSM模型进行变换,即基于语义对VSM模型的各维进行扭曲,将原本的正交坐标系基于语义变换为斜角坐标系,然后将文本的特征向量映射到变换后的VSM模型上再进行聚类,相对减小语义相关的特征向量间的语义距离,从而提高了文...     

基于XML和N层VSM的Web信息检索

基于XML文档格式良好、层次清晰,可以方便地操纵、分析其结构的特点。文中在将Web上的HTML文档转化为XML文档的基础上,通过Java中的DOM树,分析文档的层次结构。把文档分为层次化的文本段,对传统的VSM算法进行改进,把每个文本段转换为空间向量,实现了N层VSM算法,通过试验证明,改进后算法的查全率和查准率都要优于传统的VSM算法。     

基于词条属性聚类的文本特征选择算法

文本挖掘之前首先要对文本集进行有效的特征选择,传统的特征选择算法在维数约减及文本表征方面效果有限,并且因需要用到文本的类别信息而不适用于无监督的文本聚类任务。针对这种情况,设计一种适用于文本聚类任务的特征选择算法,提出词条属性的概念,首先基于词频、文档频、词位置及词间关联性构建词条特征模型,重点研究了词位置属性及词间关联性属性的权值计算方法,改进了Apriori算法用于词间关联性属性权值计算,然后通过改进的k-means聚类算法对词条特征模型进行多次聚类完成文本特征选择。实验结果表明,与传统特征选择算法相比,该算法获得较好维数约减率的同时提高了所选特征词的文本表征能力,能有效适用于文本聚类任务。     

Genetic algorithm for text clustering based on latent semantic indexing

In this paper, we develop a genetic algorithm method based on a latent semantic model (GAL) for text clustering. The main difficulty in the application of genetic algorithms (GAs) for document clustering is thousands or even tens of thousands of dimensions in feature space which is typical for textual data. Because the most straightforward and popular approach represents texts with the vector space model (VSM), that is, each unique term in the vocabulary represents one dimension. Latent semantic indexing (LSI) is a successful technology in information retrieval which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. Meanwhile, LSI takes into account the effects of synonymy and polysemy, which constructs a semantic structure in textual data. GA belongs to search techniques that can efficiently evolve the optimal solution in the reduced space. We propose a variable string length genetic algorithm which has been exploited for automatically evolving the proper number of clusters as well as providing near optimal data set clustering. GA can be used in conjunction with the reduced latent semantic structure and improve clustering efficiency and accuracy. The superiority of GAL approach over conventional GA applied in VSM model is demonstrated by providing good Reuter document clustering results.     

Content Tree Word Embedding for document representation

Only humans can understand and comprehend the actual meaning that underlies natural written language, whereas machines can form semantic relationships only after humans have provided the parameters that are necessary to model the meaning. To enable computer models to access the underlying meaning in written language, accurate and sufficient document representation is crucial. Recently, word embedding approaches have drawn much attention in text mining research. One of the main benefits of such approaches is the use of global corpuses with the generation of pre-trained word vectors. Although very effective, these approaches have their disadvantages. Relying only on pre-trained word vectors may neglect the local context and increase word ambiguity. In this study, a new approach, Content Tree Word Embedding (CTWE), is introduced to mitigate the risk of word ambiguity and inject a local context into globally pre-trained word vectors. CTWE is basically a framework for document representation while using word embedding feature learning. The CTWE structure is locally learned from training data and ultimately represents the local context. While CTWE is constructed, each word vector is updated based on its location in the content tree. For the task of classification, the results show an improvement in F-score and accuracy measures when using two deep learning-based word embedding approaches, namely GloVe and Word2Vec.     

一种基于词义和词频的向量空间模型改进方法

向量空间模型(VSM)是一种使用特征向量对文本进行建模的方法,广泛应用于文本分类、模式识别等领域。但文本内容较多时,传统的VSM建模可能产生维数爆炸现象,效率低下且难以保证分类效果。针对VSM高维现象,提出一种利用词义和词频降低文本建模维度的方法,以提高效率和准确度。提出一种多义词判别优化的同义词聚类方法,结合上下文判别多义词的词义后,根据特征项词义相似度进行加权,合并词义相近的特征项。新方法使特征向量维度大大降低,多义词判别提高了文章特征提取的准确性。与其他文本特征提取和文本分类方法进行比较,结果表明,该算法在效率和准确度上有明显提高。     

基于矩阵加权关联规则的核模糊聚类

提出一种基于矩阵加权关联规则的空间粒度聚类算法。该算法核心思想是根据文档特征向量矩阵提取文档的相似度,再在该关联规则算法上进行聚类来寻找相似关系的频繁项集。通过引入核函数,样本点被非线性变换映射到高维特征空间进行聚类,提高聚类性能。通过矩阵加权关联规则算法进行聚类。通过实验表明,在处理中小型文档时,该算法的精确度优于传统Apriori算法和K-mean算法;在处理大型文档时,该算法的时间复杂度小于传统的K-mean算法。     

基于N-Level VSM在Web信息检索中的研究

分析了传统向量空间检索模型在Web信息检索中的不足,给出了基于N-Level向量空间模型,这种模型是将一篇文档从逻辑上划分为N个相对独立的文本段,然后按照文本段的内容建立文本特征向量以及文本权值向量,在此基础上可以更加精确地定义特征值向量和相似度的计算方法,使之能比较好地适应文档集合的动态扩充。同时进行了两种模型算法时间的复杂度的比较分析。理论分析和实验结果表明,基于此模型实现的信息检索算法具有较快的查找速度和较高的查准率。     

融合BERT和自编码网络的短文本聚类研究

短文本相比于长文本词汇的数量更少,提取其中的语义特征信息更加困难,利用传统的向量空间模型VSM(vector space model)向量化表示,容易得到高维稀疏的向量.词的稀疏表示缺少语义相关性,造成语义鸿沟,从而导致下游聚类任务中,准确率低下,容易受噪声干扰等问题.提出一种新的聚类模型BERT_AE_K-Means...     

A clustering based feature selection method in spectro-temporal domain for speech recognition

Spectro-temporal representation of speech has become one of the leading signal representation approaches in speech recognition systems in recent years. This representation suffers from high dimensionality of the features space which makes this domain unsuitable for practical speech recognition systems. In this paper, a new clustering based method is proposed for secondary feature selection/extraction in the spectro-temporal domain. In the proposed representation, Gaussian mixture models (GMM) and weighted K-means (WKM) clustering techniques are applied to spectro-temporal domain to reduce the dimensions of the features space. The elements of centroid vectors and covariance matrices of clusters are considered as attributes of the secondary feature vector of each frame. To evaluate the efficiency of the proposed approach, the tests were conducted for new feature vectors on classification of phonemes in main categories of phonemes in TIMIT database. It was shown that by employing the proposed secondary feature vector, a significant improvement was revealed in classification rate of different sets of phonemes comparing with MFCC features. The average achieved improvements in classification rates of voiced plosives comparing to MFCC features is 5.9% using WKM clustering and 6.4% using GMM clustering. The greatest improvement is about 7.4% which is obtained by using WKM clustering in classification of front vowels comparing to MFCC features.     

文本聚类中权重计算的对偶性策略

在文本聚类/分类处理中,一个重要步骤就是寻找文本的合理表示.在被广泛采用的向量空间模型中,一个文本被表示成一个向量,向量的各维是特征项,而向量空间模型的核心问题就是如何进行特征的抽取和选择.在特征的权重计算中,存在一种对偶性现象.利用迭代的方法来处理和利用这种对偶性,获得了文本的隐含概念.实验结果表明,采用概念空间代替原始词空间来表示文本,能够得到更好的聚类结果.