Traditional Vector Space Research Articles

Automatic classification of document resources based on Naive Bayesian classification algorithm

World Wide Web has become big as the amount of documents collection is increasing rapidly. The automatic classification of document resources based on Naive Bayesian classification algorithm is detailed in this paper. Firstly, this paper introduces the relevant theories of naive Bayes classification and the automatic document classification system. Then, a massive network academic document automatic classification system is designed and implemented. The system uses modular design, including academic document automatic capture module, academic document word document matrix processing module, ontology integration module and semantic driven classification module. Finally, based on the Naive Bayesian classification algorithm, the training set of 12 categories preset is utilized in the professional classification directory of the Ministry of education.. Experiments show that the naive Bayesian classification algorithm can effectively complete the automatic capture, processing and classification of massive academic documents, which can not only improve the classification accuracy, but also reduce the running time of automatic classification. It solves the problems of the integration of two heterogeneous ontology libraries and also the problem that the traditional word vector space cannot meet people's needs for semantic classification.

Text Clustering and Economic Analysis of Free Trade Zone Governance Strategies Based on Random Matrix and Subject Analysis

The steps of generating basic data by the LDA model and calculating text by the weighted algorithm have a good effect on text clustering. In this paper, the LDA topic model is used to effectively improve the accuracy of strategy text clustering. FTZ economics text clustering simulates FTA economics text data and economic data, imports economics and economic figures and word lists, and uses the traditional vector space model for factor representation. After that, the text vectors are independent of each other, ignoring the semantic relationship, which affects the clustering analysis results. A Chinese text clustering algorithm based on semantic clustering is proposed. Based on the principle of cooccurrence and semantic relevance of words, the algorithm uses the collocation vector of feature words to construct semantic clustering; find the document vector with embedded semantic information. Finally, document vectors with embedded semantic information are used. Finally, K vector is used for cluster analysis. The simulation analysis in this paper shows that the economic growth of the free trade zone is the largest under the economics guidance, which can reach 15%.

Text Feature Extraction for Public English Vocabulary Based on Wavelet Transform.

Text interpretation of public English vocabulary is a critical task in the subject of natural language processing, which uses technology to allow humans and computers to communicate effectively using natural language. Text feature extraction is one of the most fundamental and crucial elements in allowing computers to effectively grasp and read text. This paper proposes a text feature extraction method based on wavelet analysis that performs fast discrete wavelet transform and inverse discrete wavelet transform on the feature vectors under the traditional TF-IDF vector space model to address the problem of low feature differentiation of high-dimensional data in text feature extraction. In particular, due to the design of the Mallat algorithm, there is frequency aliasing in the signal decomposition process. This phenomenon is a problem that cannot be ignored when using wavelet analysis for feature extraction. Therefore, this paper proposes an improved inverse discrete wavelet transform method, in which the signal is decomposed by Mallat algorithm to obtain wavelet coefficients at each scale and then reconstructed to the required wavelet space coefficients according to the reconstruction method, and the reconstructed coefficients are used to analyze the signal at that scale instead of the wavelet coefficients obtained at the corresponding scale. Experiments on the public English vocabulary dataset reveal that the wavelet transform-based strategy suggested in this research outperforms existing feature extraction methods while maintaining greater classification accuracy while reducing the dimensionality of the TF-IDF vector space model.

Intelligent Analysis and Positioning of Political Public Opinion in Universities

With the rapid development of Internet technology, the network has become an indispensable way of life for undergraduates. The correct guidance of public opinion has also become an important thing in the ideological work of universities. Undergraduates are in an important period of formation and development of thoughts that they are easily to be incited by cyber-rumors. Therefore, it is particularly important to obtain the data of political public opinion in universities and position the hot topics for early detection of political public opinion tendency, which can also avoid the outbreak of major security incidents. With such consideration, this paper obtains multi-source political public opinion data from BBS, Tieba and Weibo of SUN YAT-SEN UNIVERSITY (SYSU) through crawler. We study a text feature extraction method based on Word2Vec & LDA (Latent Dirichlet Allocation), which improves the high-dimensional sparsity in traditional Vector Space Model (VSM) text representation. Meanwhile, based on the classical Single-pass clustering algorithm, this paper studies the Single-pass & HAC clustering algorithm. In addition, a measurement method of hot topic is defined to calculate the heat value of political public opinion. Dictionary and rule based method is used to improve the accuracy of sentiment tendency analysis. The experimental results demonstrate that the effect of topic detection and positioning based on LDA & Word2Vec and Single-pass & HAC algorithm is better than other methods.

Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

Clustering is an important unsupervised analysis technique for big data mining. It finds its application in several domains including biomedical documents of the MEDLINE database. Document clustering algorithms based on metaheuristics is an active research area. However, these algorithms suffer from the problems of getting trapped in local optima, need many parameters to adjust, and the documents should be indexed by a high dimensionality matrix using the traditional vector space model. In order to overcome these limitations, in this paper a new documents clustering algorithm (ASOS-LSI) with no parameters is proposed. It is based on the recent symbiotic organisms search metaheuristic (SOS) and enhanced by an acceleration technique. Furthermore, the documents are represented by semantic indexing based on the famous latent semantic indexing (LSI). Conducted experiments on well-known biomedical documents datasets show the significant superiority of ASOS-LSI over five famous algorithms in terms of compactness, f-measure, purity, misclassified documents, entropy, and runtime.

EWNStream+: Effective and Real-time Clustering of Short Text Streams Using Evolutionary Word Relation Network

The real-time clustering of short text streams has various applications, such as event tracking, text summarization and sentimental analysis. However, accurately and efficiently clustering short text streams is challenging due to the sparsity problem (i.e., the limited information comprised in a single short text document leads to high-dimensional and sparse vectors when we represent short texts using traditional vector space models), topic drift and the fast generated text streams. In this paper, we provide an effective and real-time Evolutionary Word relation Network for short text streams clustering (EWNStream[Formula: see text]) method. The EWNStream[Formula: see text] method constructs a bi-weighted word relation network using the aggregated term frequencies and term co-occurrence statistics at corpus level to overcome the sparsity problem and topic drift of short texts. Better still, as the query window in the stream shifts to the newly arriving data, EWNStream[Formula: see text] is capable of incrementally updating the word relation network by incorporating new word statistics and decaying the old ones to naturally capture the underlying topic drift in the data streams and reduce the size of the network. The experimental results on a real-world dataset show that EWNStream[Formula: see text] can achieve better clustering accuracy and time efficiency than several counterpart methods.

Combining Fact Extraction and Verification with Neural Semantic Matching Networks

The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evidential sentences from Wikipedia documents. In this paper, we present a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification. For evidence retrieval (document retrieval and sentence selection), unlike traditional vector space IR models in which queries and sources are matched in some pre-designed term vector space, we develop neural models to perform deep semantic matching from raw textual input, assuming no intermediate term representation and no access to structured external knowledge bases. We also show that Pageview frequency can also help improve the performance of evidence retrieval results, that later can be matched by using our neural semantic matching network. For claim verification, unlike previous approaches that simply feed upstream retrieved evidence and the claim to a natural language inference (NLI) model, we further enhance the NLI model by providing it with internal semantic relatedness scores (hence integrating it with the evidence retrieval modules) and ontological WordNet features. Experiments on the FEVER dataset indicate that (1) our neural semantic matching method outperforms popular TF-IDF and encoder models, by significant margins on all evidence retrieval metrics, (2) the additional relatedness score and WordNet features improve the NLI model via better semantic awareness, and (3) by formalizing all three subtasks as a similar semantic matching problem and improving on all three stages, the complete model is able to achieve the state-of-the-art results on the FEVER test set (two times greater than baseline results).1

Application of improved distributed naive Bayesian algorithms in text classification

The naive Bayes classifier is a widely used text classification method that applies statistical theory to text classification. Due to the particularity of the text, related feature items may generate new semantic information, which may be lost when the traditional vector space model represents text. This paper mainly studies the construction and improvement of distributed naive Bayes automatic classification system. The application of Hadoop cloud computing in web page classification is one of the focuses of this article. Firstly, the text classification system and Bayesian classification model are analyzed and discussed, including the representation and extraction of text information, text classification methods and Bayesian text classification methods. Then, in view of the shortcomings of the above-mentioned naive Bayesian text classification method, when training text, we use the mutual information method to check the correlation between the feature sets generated after feature selection, and then combine the features with higher correlation degree appropriately. Through a series of tests, the experimental data show that the improved text classification system can achieve better classification results.

Suitability and importance of deep learning feature space in the domain of text categorisation

One of the important features of multilayer ELM (ML-ELM) is its capability of nonlinearly mapping the features to an extended dimensional space and thereby builds the input features linearly separable. This paper studies the significance of deep learning feature space using ML-ELM for classification of text data which is the follow-up of my earlier approach. The previous approach discusses a new feature selection technique named combined cohesion separation and silhouette coefficient (CCSS) to generate a good feature vector and then used it for classification of text data using ML-ELM, which is a deep learning classifier. That approach is extended here which has two main aspects. The first aspect is to compare the performance of CCSS approach with the traditional feature selection techniques and the second aspect is to test the performance of different conventional classification techniques on the higher dimensional feature space of ML-ELM. Results of the experiment on different benchmark datasets justify that the proposed CCSS technique is comparable with the existing feature selection techniques and the ML-ELM feature space is more promising compared to the traditional TF-IDF vector space for classification of text data.

Suitability and importance of deep learning feature space in the domain of text categorisation

One of the important features of multilayer ELM (ML-ELM) is its capability of nonlinearly mapping the features to an extended dimensional space and thereby builds the input features linearly separable. This paper studies the significance of deep learning feature space using ML-ELM for classification of text data which is the follow-up of my earlier approach. The previous approach discusses a new feature selection technique named combined cohesion separation and silhouette coefficient (CCSS) to generate a good feature vector and then used it for classification of text data using ML-ELM, which is a deep learning classifier. That approach is extended here which has two main aspects. The first aspect is to compare the performance of CCSS approach with the traditional feature selection techniques and the second aspect is to test the performance of different conventional classification techniques on the higher dimensional feature space of ML-ELM. Results of the experiment on different benchmark datasets justify that the proposed CCSS technique is comparable with the existing feature selection techniques and the ML-ELM feature space is more promising compared to the traditional TF-IDF vector space for classification of text data.

Practical Text Phylogeny for Real-World Settings

The ease with which one can edit and redistribute digital documents on the Internet is one of modernity’s great achievements, but it also leads to some vexing problems. With growing academic interest in the study of the evolution of digital writing on the one hand and the rise of disinformation on the other, the problem of identifying the relationship between texts with similar content is becoming more important. Traditional vector space representations of texts have made progress in solving this problem when it is cast as a reconstruction task that organizes related texts into a tree expressing relationships—this is dubbed text phylogeny in the information forensics literature. However, as new text representation methods have been successfully applied to many other text analysis problems, it is worth investigating if they too are used in text phylogeny tree reconstruction. In this paper, we explore the use of word embeddings as a text representation method, with the aim of trying to improve the accuracy of reconstructed phylogeny trees for real-world data and compare it with other widely used text representation methods. We evaluate the performance on established benchmarks for this task: a synthetic data set and data collected from Wikipedia. We also apply our framework to a new data set of fan fiction based on some famous fairy tales. Experimental results show that word embeddings are competitive with other feature sets for the published benchmarks, and are highly effective for creative writing.

Predicting users’ demographic characteristics in a Chinese social media network

PurposeBased on user-generated content from a Chinese social media platform, this paper aims to investigate multiple methods of constructing user profiles and their effectiveness in predicting their gender, age and geographic location.Design/methodology/approachThis investigation collected 331,634 posts from 4,440 users of Sina Weibo. The data were divided into two parts, for training and testing . First, a vector space model and topic models were applied to construct user profiles. A classification model was then learned by a support vector machine according to the training data set. Finally, we used the classification model to predict users’ gender, age and geographic location in the testing data set.FindingsThe results revealed that in constructing user profiles, latent semantic analysis performed better on the task of predicting gender and age. By contrast, the method based on a traditional vector space model worked better in making predictions regarding the geographic location. In the process of applying a topic model to construct user profiles, the authors found that different prediction tasks should use different numbers of topics.Originality/valueThis study explores different user profile construction methods to predict Chinese social media network users’ gender, age and geographic location. The results of this paper will help to improve the quality of personal information gathered from social media platforms, and thereby improve personalized recommendation systems and personalized marketing.

Kernel-based consensus clustering for ontology-embedded document repository of power substations

This paper presents a novel consensus clustering (CC) approach for a document repository concerning power substations (PSD) and contributes to the intangible asset management of power systems. A domain ontology model, i.e., substation ontology (SONT), is applied to modify the traditional vector space model (VSM) for document representation, which is concerned with the semantic relationship between terms. A new document representation is generated using a term mutual information matrix with the aid of SONT. In addition, compared with two other novel CC algorithms, i.e., non-negative matrix factorisation-based CC (NNMF-CC) and information theory-based CC (INT-CC), weighted partition via kernel-based CC algorithm (WPK-CC) is utilised to solve the CC issue for PSD. Meanwhile, genetic algorithms (GA) were applied to WPK-CC for PSD, as there are limitations in the original WPK-CC for document clustering. Subsequently, selected mechanisms in each GA’s procedure are compared and improved, resulting in comprehensive parameter settings for the PSD CC. Four simulation studies have been designed, in which the results are evaluated by purity validation method and show that the SONT-based document representation and improved WPK-CC, via modified GA, significantly improve the performance of the PSD CC.

Semantic association ranking schemes for information retrieval applications using term association graph representation

Most of the Information Retrieval (IR) techniques are based on representing the documents using the traditional vector space and probabilistic language model i.e., bag-of- words model. In this paper, associations among words in the documents are assessed and it is expressed in Term Association Graph model to represent the document content and the relationship among the keywords. Earlier attempt on exploiting term association graph was done for non-personalized document re-ranking task. This paper experiments improved non-personalized and personalized re-ranking strategy which exploits term association graph data structure to assess the importance of a document for the user query and thus documents are re-ranked according to the association and similarity exists among the documents. This paper proposes various approaches under two models namely, Term Rank based Approach (TRA) and Path Traversal based Approaches (PTA1, PTA2, and PTA3). These approaches employ term association graph and has been evaluated using manually prepared real dataset and benchmark OHSUMED dataset. The results obtained are reasonably promising.

A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

Semi-supervised evolutionary ensembles for Web video categorization

Evolutionary Algorithms (EA) have been developing rapidly as a powerful and general learning approach which has been used successfully to find a reasonable solution for data mining and knowledge discovery. Genetic algorithm (GA) is a kind of mainstream EA paradigm with a purpose of developing solutions for optimization problems. Clustering ensembles have emerged as an outstanding algorithm in machine learning to leverage the consensus across multiple clustering solutions and combines their predictions into a single solution with improved robustness, stability and accuracy. Multimedia advancement and popularity of the social Web has collectively provided an easy way to generate bulk of videos. Categorization of such Web videos has become a hot research challenge. In this paper, we propose a Semi-supervised Evolutionary Ensemble (SS-EE) framework for social media mining, e.g., Web Video Categorization (WVC), using their low cost textual features, intrinsic relations and extrinsic Web support. The contributions of this research work are as follows. First, we extend the traditional Vector Space Model (VSM) to Semantic VSM (S-VSM) by considering the semantic similarity between the feature terms using Normalized Google Distance (NGD) approach. Second, we define a new distance measure, Triangular Similarity (TrS) between two Textual Feature Vectors (TFV) based on the frequencies of most relevant terms in each category. Third, we iterate the clustering ensemble process with the help of GA guided by a new measure, Pre-Paired Percentage (PPP), to be used as the fitness function during the genetic cycle. Fourth, in the key steps of the GA, crossover and mutation genetic operators, we define them by an intelligent mechanism of clustering ensemble. Fifth, in order to terminate the genetic cycle, we define another new measure, Clustering Quality (Cq), based on similarity matrix and clustering labels. Experiments on real world social-Web data (YouTube) have been performed to validate the SS-EE framework.

Information Ordering with an Event‐Enriched Vector Space Model for Multi‐Document News Summarization

Information ordering is a nontrivial task in multi‐document summarization (MDS), which typically relies on the traditional vector space model (VSM) notorious for semantic deficiency. In this article, we propose a novel event‐enriched VSM to alleviate the problem by building event semantics into sentence representations. The mediation of event information between sentence and term, especially in the news domain, has an intuitive appeal as well as technical advantage in common sentence‐level operations such as sentence similarity computation. Inspired by the block‐style writing by humans, we base the sentence ordering algorithm on sentence clustering. To accommodate the complexity introduced by event information, we adopt a soft‐to‐hard clustering strategy on the event and sentence levels, using expectation–maximization clustering and K‐means, respectively. For the purpose of cluster‐based sentence ordering, the event‐enriched VSM enables us to design an ordering algorithm to enhance event coherence computed between sentence and sentence–context pairs. Drawing on the findings of earlier research, we also incorporate topic continuity measures and time information into the scheme. We evaluate the performance of the model and its variants automatically and manually, with experimental results showing clear advantage of the event‐based model over baseline and non‐event‐based models in information ordering for multi‐document news summarization. We are confident that the event‐enriched VSM has even greater potential in summarization and beyond, which awaits further research. © 2014 Wiley Periodicals, Inc.

A Novel Method for Text Similarity Calculation

In view of the fact that traditional vector space model for text similarity calculation which does not take word order into consideration leads to bias, this paper puts forward a longest common subsequence and the traditional vector space model of combining text similarity calculation. This method takes the word order and word frequency information into account, using the texts of the longest common subsequence and substring of their information from all public records and the use of word order and word frequency in the text. The importance of similarity calculation is acknowledged, and the traditional vector space model in the calculation of the weight is used on the word frequency information. Some of the dataset collected through the web crawler are used in the proposed text similarity calculation method for testing, and the results proved the effectivity of the method.

On Multi Class Vector Space Model-based Information Retrieval

A new model of information retrieval algorithms, multi class vector space model, is proposed in this paper based on traditional vector space model. Web document has semi structured characteristic. The keyword or terms that are used for indexing purpose in any location, so content of this location represent important information in the web documents. Vector space model ignores the importance of these terms with respect to their position while calculating the weight of the indexing terms. The experimental result shows that this method can further improve the performance of vector space model, save storage space and speed up the retrieval speed with high precision and recall rate.

LDA boost classification: boosting by topics

AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naive Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.