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Text classification (TC) is a task that assigns a text to one or more classes and predefined categories. Constructing text classifiers with high accuracy is a vital task in biomedical field, given the wealth of information hidden in unlabelled documents. Because of large feature spaces, traditionally discriminative approaches, such as logistic regression and support vector machines with n-gram and...
With the development of technology, people are entering the virtual world more and more. Parallel to this, the internet becomes a bigger network every day and it gets a complex structure depending on this growth. Achieving the desired information with structred data becomes an increasingly important problem. One of the useful ways to find solution for this problem is to divide this complex data into...
This work includes processing and classification of tweets which are written in Turkish language. Four different sector tweet datasets are vectorized with Word Embedding model and classified with Support Vector Machine and Random Forests classifiers and results have been compared. We have showed that sector based tweet classification is more successful compared to general tweets. Accuracy rates for...
In the text classification, The similarity between the text need to be calculated, but the existing classification methods only consider the similarity between feature words and categories and does not involve the semantic similarity between feature words. In this paper, a new classification model LDA (Latent Dirichlet Allocation) — KNN (K-Nearest Neighbor) is proposed. LDA is used to solve the problem...
The classification of text documents into a number of pre-defined categories has many application scenarios, for example the classification of news items into thematic sections. Documents to be classified are commonly represented by a bag-of-words feature vector. The bag-of-words model cannot handle two language phenomena: synonymy and polysemy, besides, dimensions of feature vectors are orthogonal...
Feature selection, which aims at obtaining a compact and effective feature subset for better performance and higher efficiency, has been studied for decades. The traditional feature selection metrics, such as Chi-square and information gain, fail to consider how important a feature is in a document. Features, no matter how much effective semantic information they hold, are treated equally. Intuitively,...
We present a novel approach to semi-supervised learning for text classification based on the higher-order co-occurrence paths of words. We name the proposed method as Semi-Supervised Semantic Higher-Order Smoothing (S3HOS). The S3HOS is built on a tri-partite graph based data representation of labeled and unlabeled documents that allows semantics in higher-order co-occurrence paths between terms (words)...
This paper presents a semantic naïve Bayes classification technique that is based upon our tensor space model for text representation. In our work, each of Wikipedia articles is defined as a single concept, and a document is represented as a 2nd-order tensor. Our method expands the conventional naïve Bayes by incorporating the semantic concept features into term feature statistics under the tensor-space...
The key of big text documents data analysis is to classify those text documents. To classify those text documents, it is necessary to represent those text documents as vectors which is vector space model (VSM). A powerful vector space model should remain the classification information with dimensions as little as possible. To achieve that, it is important to select most effective features for text...
Spam has been a serious and annoying problem for decades. Even though plenty of solutions have been put forward, there still remains a lot to be promoted in filtering spam emails more efficiently. Nowadays a major problem in spam filtering as well as text classification in natural language processing is the huge size of vector space due to the numerous feature terms, which is usually the cause of...
Feature selection is a strategy that aims at making text classifiers more efficient and accurate. In this paper, we proposed a novel feature selection method based on Tibetan grammar for Tibetan classification. Tibetan language express grammatical meaning through the function words and word order, and the function word has large proportions. By analyzing the Tibetan grammar and distribution of part...
The given paper describes modern approach to the task of sentiment analysis of movie reviews by using deep learning recurrent neural networks and decision trees. These methods are based on statistical models, which are in a nutshell of machine learning algorithms. The fertile area of research is the application of Google's algorithm Word2Vec presented by Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey...
Text categorization is an important research in nature language process and content analysis. In this paper, we present latent factor SVM (LF-SVM) for text categorization which use latent factor vectors for category representation on text categorization. We prove that latent factors extracted by PLSA (probability latent semantic analysis) can span convex structure to express text category. Based on...
Classification is a technique in data mining for categorizing objects. Text Classification is re-challenged for classifying very short documents or text as shown in social media collection. This paper proposes a method to improve the performance of classification on short documents. In this work, we expand words in every document before the documents are classified We use TFIDF model, Hidden Markov...
The bag of words (BOW) representation of documents is very common in text classification systems. However, the BOW approach ignores the position of the words in the document and more importantly, the semantic relations between the words. In this study, we present a simple semantic kernel for Support Vector Machines (SVM) algorithm. This kernel uses higher-order relations between terms in order to...
In conventional text categorization algorithms, documents are symbolized as “bag of words” (BOW) with the fact that documents are supposed to be independent from each other. While this approach simplifies the models, it ignores the semantic information between terms of each document. In this study, we develop a novel method to measure semantic similarity based on higher-order dependencies between...
This paper addresses a new text classification method: Sparse Topic Model, which represents documents by the sparse coding of topics. Topics contain more semantic information than words, so it's more effective for feature representation of documents. Topics are extracted from documents by LDA in an unsupervised way. Based on these topics, sparse coding is applied to discover more high-level representation...
The RLS-MARS (Regularized Least Squares-Multi Angle Regression and Shrinkage) feature selection model is used to select the relevant information, in which both, the keeping and the leaving-out of the regularizer are present. The RLS-MARS model is to find a series of directions in multidimensional space, leading the gradient vectors to change along those directions which would make the gradient matrix's...
This paper proposes an approach for mining the semantic relationships between terms. Using a dependency model based on syntactic parsing, the syntactic features of a term are first extracted from large scale corpus, and then the vector representation for this term is constructed. By the cosine similarities between vectors, we can get the semantically related words for a term. We apply the semantic...
In order to overcome the SVM for text classification ignoring the context of semantic information and the use of a community to text classification, one boundary point can only belong to a community of view, the concept of contribution and overlapping coefficient based on the complex network diagram is introduced. And feature selection algorithm based on community discovery is proposed. Experiments...
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