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1.
Cross-lingual text categorization: Conquering language boundaries in globalized environments 总被引：1，自引：0，他引：1
Text categorization pertains to the automatic learning of a text categorization model from a training set of preclassified documents on the basis of their contents and the subsequent assignment of unclassified documents to appropriate categories. Most existing text categorization techniques deal with monolingual documents (i.e., written in the same language) during the learning of the text categorization model and category assignment (or prediction) for unclassified documents. However, with the globalization of business environments and advances in Internet technology, an organization or individual may generate and organize into categories documents in one language and subsequently archive documents in different languages into existing categories, which necessitate cross-lingual text categorization (CLTC). Specifically, cross-lingual text categorization deals with learning a text categorization model from a set of training documents written in one language (e.g., L1) and then classifying new documents in a different language (e.g., L2). Motivated by the significance of this demand, this study aims to design a CLTC technique with two different category assignment methods, namely, individual- and cluster-based. Using monolingual text categorization as a performance reference, our empirical evaluation results demonstrate the cross-lingual capability of the proposed CLTC technique. Moreover, the classification accuracy achieved by the cluster-based category assignment method is statistically significantly higher than that attained by the individual-based method. 相似文献
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3.
Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally speaking, it is one of the most important methods to organize and make use of the gigantic amounts of information that exist in unstructured textual format. Text classification is a widely studied research area of language processing and text mining. In traditional text classification, a document is represented as a bag of words where the words in other words terms are cut from their finer context i.e. their location in a sentence or in a document. Only the broader context of document is used with some type of term frequency information in the vector space. Consequently, semantics of words that can be inferred from the finer context of its location in a sentence and its relations with neighboring words are usually ignored. However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances. Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification. In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. This survey explores the past and recent advancements in semantic text classification and attempts to organize existing approaches under five fundamental categories; domain knowledge-based approaches, corpus-based approaches, deep learning based approaches, word/character sequence enhanced approaches and linguistic enriched approaches. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms. 相似文献
4.
Chun-Yan Liang Li GuoZhao-Jie Xia Feng-Guang NieXiao-Xia Li Liang SuZhang-Yuan Yang 《Information processing & management》2006
A new dictionary-based text categorization approach is proposed to classify the chemical web pages efficiently. Using a chemistry dictionary, the approach can extract chemistry-related information more exactly from web pages. After automatic segmentation on the documents to find dictionary terms for document expansion, the approach adopts latent semantic indexing (LSI) to produce the final document vectors, and the relevant categories are finally assigned to the test document by using the k-NN text categorization algorithm. The effects of the characteristics of chemistry dictionary and test collection on the categorization efficiency are discussed in this paper, and a new voting method is also introduced to improve the categorization performance further based on the collection characteristics. The experimental results show that the proposed approach has the superior performance to the traditional categorization method and is applicable to the classification of chemical web pages. 相似文献
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This paper examines several different approaches to exploiting structural information in semi-structured document categorization. The methods under consideration are designed for categorization of documents consisting of a collection of fields, or arbitrary tree-structured documents that can be adequately modeled with such a flat structure. The approaches range from trivial modifications of text modeling to more elaborate schemes, specifically tailored to structured documents. We combine these methods with three different text classification algorithms and evaluate their performance on four standard datasets containing different types of semi-structured documents. The best results were obtained with stacking, an approach in which predictions based on different structural components are combined by a meta classifier. A further improvement of this method is achieved by including the flat text model in the final prediction. 相似文献
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Authorship analysis of electronic texts assists digital forensics and anti-terror investigation. Author identification can be seen as a single-label multi-class text categorization problem. Very often, there are extremely few training texts at least for some of the candidate authors or there is a significant variation in the text-length among the available training texts of the candidate authors. Moreover, in this task usually there is no similarity between the distribution of training and test texts over the classes, that is, a basic assumption of inductive learning does not apply. In this paper, we present methods to handle imbalanced multi-class textual datasets. The main idea is to segment the training texts into text samples according to the size of the class, thus producing a fairer classification model. Hence, minority classes can be segmented into many short samples and majority classes into less and longer samples. We explore text sampling methods in order to construct a training set according to a desirable distribution over the classes. Essentially, by text sampling we provide new synthetic data that artificially increase the training size of a class. Based on two text corpora of two languages, namely, newswire stories in English and newspaper reportage in Arabic, we present a series of authorship identification experiments on various multi-class imbalanced cases that reveal the properties of the presented methods. 相似文献
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Automatic text classification is the problem of automatically assigning predefined categories to free text documents, thus allowing for less manual labors required by traditional classification methods. When we apply binary classification to multi-class classification for text classification, we usually use the one-against-the-rest method. In this method, if a document belongs to a particular category, the document is regarded as a positive example of that category; otherwise, the document is regarded as a negative example. Finally, each category has a positive data set and a negative data set. But, this one-against-the-rest method has a problem. That is, the documents of a negative data set are not labeled manually, while those of a positive set are labeled by human. Therefore, the negative data set probably includes a lot of noisy data. In this paper, we propose that the sliding window technique and the revised EM (Expectation Maximization) algorithm are applied to binary text classification for solving this problem. As a result, we can improve binary text classification through extracting potentially noisy documents from the negative data set using the sliding window technique and removing actually noisy documents using the revised EM algorithm. The results of our experiments showed that our method achieved better performance than the original one-against-the-rest method in all the data sets and all the classifiers used in the experiments. 相似文献
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文本分类是处理和组织大量文本数据的关键技术之一。为了更加有效地实现文本分类,本文提出了一种基于图模型的文本特征提取方法。该方法利用类别信息在训练数据集上构造邻接带权图及其补图,使得属于同一个类别的样本点的投影尽可能近,不属于同一个类别的样本点的投影尽可能远。这种方法既能够获得文本空间的全局结构信息又可以保留局部结构信息。最后,采用K近邻分类器在20Newsgroups标准数据集上进行训练和测试,并且与基于潜在语义索引的文本分类方法做了比较,文本分类的性能得到很大提高。实验结果表明,本文所提出的方法能够有效地提高文本分类的性能。 相似文献
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Text documents usually contain high dimensional non-discriminative (irrelevant and noisy) terms which lead to steep computational costs and poor learning performance of text classification. One of the effective solutions for this problem is feature selection which aims to identify discriminative terms from text data. This paper proposes a method termed “Hebb rule based feature selection (HRFS)”. HRFS is based on supervised Hebb rule and assumes that terms and classes are neurons and select terms under the assumption that a term is discriminative if it keeps “exciting” the corresponding classes. This assumption can be explained as “a term is highly correlated with a class if it is able to keep “exciting” the class according to the original Hebb postulate. Six benchmarking datasets are used to compare HRFS with other seven feature selection methods. Experimental results indicate that HRFS is effective to achieve better performance than the compared methods. HRFS can identify discriminative terms in the view of synapse between neurons. Moreover, HRFS is also efficient because it can be described in the view of matrix operation to decrease complexity of feature selection. 相似文献
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The automated classification of texts into predefined categories has witnessed a booming interest, due to the increased availability of documents in digital form and the ensuing need to organize them. An important problem for text classification is feature selection, whose goals are to improve classification effectiveness, computational efficiency, or both. Due to categorization unbalancedness and feature sparsity in social text collection, filter methods may work poorly. In this paper, we perform feature selection in the training process, automatically selecting the best feature subset by learning, from a set of preclassified documents, the characteristics of the categories. We propose a generative probabilistic model, describing categories by distributions, handling the feature selection problem by introducing a binary exclusion/inclusion latent vector, which is updated via an efficient Metropolis search. Real-life examples illustrate the effectiveness of the approach. 相似文献
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《Information processing & management》2020,57(6):102342
Pseudo-relevance feedback (PRF) is a well-known method for addressing the mismatch between query intention and query representation. Most current PRF methods consider relevance matching only from the perspective of terms used to sort feedback documents, thus possibly leading to a semantic gap between query representation and document representation. In this work, a PRF framework that combines relevance matching and semantic matching is proposed to improve the quality of feedback documents. Specifically, in the first round of retrieval, we propose a reranking mechanism in which the information of the exact terms and the semantic similarity between the query and document representations are calculated by bidirectional encoder representations from transformers (BERT); this mechanism reduces the text semantic gap by using the semantic information and improves the quality of feedback documents. Then, our proposed PRF framework is constructed to process the results of the first round of retrieval by using probability-based PRF methods and language-model-based PRF methods. Finally, we conduct extensive experiments on four Text Retrieval Conference (TREC) datasets. The results show that the proposed models outperform the robust baseline models in terms of the mean average precision (MAP) and precision P at position 10 (P@10), and the results also highlight that using the combined relevance matching and semantic matching method is more effective than using relevance matching or semantic matching alone in terms of improving the quality of feedback documents. 相似文献
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The number of patent documents is currently rising rapidly worldwide, creating the need for an automatic categorization system to replace time-consuming and labor-intensive manual categorization. Because accurate patent classification is crucial to search for relevant existing patents in a certain field, patent categorization is a very important and useful field. As patent documents are structural documents with their own characteristics distinguished from general documents, these unique traits should be considered in the patent categorization process. In this paper, we categorize Japanese patent documents automatically, focusing on their characteristics: patents are structured by claims, purposes, effects, embodiments of the invention, and so on. We propose a patent document categorization method that uses the k-NN (k-Nearest Neighbour) approach. In order to retrieve similar documents from a training document set, some specific components to denote the so-called semantic elements, such as claim, purpose, and application field, are compared instead of the whole texts. Because those specific components are identified by various user-defined tags, first all of the components are clustered into several semantic elements. Such semantically clustered structural components are the basic features of patent categorization. We can achieve a 74% improvement of categorization performance over a baseline system that does not use the structural information of the patent. 相似文献
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Wanpeng Song Liu Wenyin Naijie Gu Xiaojun Quan Tianyong Hao 《Information processing & management》2011
Question categorization, which suggests one of a set of predefined categories to a user’s question according to the question’s topic or content, is a useful technique in user-interactive question answering systems. In this paper, we propose an automatic method for question categorization in a user-interactive question answering system. This method includes four steps: feature space construction, topic-wise words identification and weighting, semantic mapping, and similarity calculation. We firstly construct the feature space based on all accumulated questions and calculate the feature vector of each predefined category which contains certain accumulated questions. When a new question is posted, the semantic pattern of the question is used to identify and weigh the important words of the question. After that, the question is semantically mapped into the constructed feature space to enrich its representation. Finally, the similarity between the question and each category is calculated based on their feature vectors. The category with the highest similarity is assigned to the question. The experimental results show that our proposed method achieves good categorization precision and outperforms the traditional categorization methods on the selected test questions. 相似文献
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《Information processing & management》2002,38(4):529-546
In this paper, we present an efficient text categorization algorithm that generates bigrams selectively by looking for ones that have an especially good chance of being useful. The algorithm uses the information gain metric, combined with various frequency thresholds. The bigrams, along with unigrams, are then given as features to two different classifiers: Naı̈ve Bayes and maximum entropy. The experimental results suggest that the bigrams can substantially raise the quality of feature sets, showing increases in the break-even points and F1 measures. The McNemar test shows that in most categories the increases are very significant. Upon close examination of the algorithm, we concluded that the algorithm is most successful in correctly classifying more positive documents, but may cause more negative documents to be classified incorrectly. 相似文献
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《Information processing & management》2020,57(6):102341
Today, due to a vast amount of textual data, automated extractive text summarization is one of the most common and practical techniques for organizing information. Extractive summarization selects the most appropriate sentences from the text and provide a representative summary. The sentences, as individual textual units, usually are too short for major text processing techniques to provide appropriate performance. Hence, it seems vital to bridge the gap between short text units and conventional text processing methods.In this study, we propose a semantic method for implementing an extractive multi-document summarizer system by using a combination of statistical, machine learning based, and graph-based methods. It is a language-independent and unsupervised system. The proposed framework learns the semantic representation of words from a set of given documents via word2vec method. It expands each sentence through an innovative method with the most informative and the least redundant words related to the main topic of sentence. Sentence expansion implicitly performs word sense disambiguation and tunes the conceptual densities towards the central topic of each sentence. Then, it estimates the importance of sentences by using the graph representation of the documents. To identify the most important topics of the documents, we propose an inventive clustering approach. It autonomously determines the number of clusters and their initial centroids, and clusters sentences accordingly. The system selects the best sentences from appropriate clusters for the final summary with respect to information salience, minimum redundancy, and adequate coverage.A set of extensive experiments on DUC2002 and DUC2006 datasets was conducted for investigating the proposed scheme. Experimental results showed that the proposed sentence expansion algorithm and clustering approach could considerably enhance the performance of the summarization system. Also, comparative experiments demonstrated that the proposed framework outperforms most of the state-of-the-art summarizer systems and can impressively assist the task of extractive text summarization. 相似文献
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《Information processing & management》2020,57(2):102034
Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures. 相似文献
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《Information processing & management》2023,60(2):103215
Traditional topic models are based on the bag-of-words assumption, which states that the topic assignment of each word is independent of the others. However, this assumption ignores the relationship between words, which may hinder the quality of extracted topics. To address this issue, some recent works formulate documents as graphs based on word co-occurrence patterns. It assumes that if two words co-occur frequently, they should have the same topic. Nevertheless, it introduces noise edges into the model and thus hinders topic quality since two words co-occur frequently do not mean that they are on the same topic. In this paper, we use the commonsense relationship between words as a bridge to connect the words in each document. Compared to word co-occurrence, the commonsense relationship can explicitly imply the semantic relevance between words, which can be utilized to filter out noise edges. We use a relational graph neural network to capture the relation information in the graph. Moreover, manifold regularization is utilized to constrain the documents’ topic distributions. Experimental results on a public dataset show that our method is effective at extracting topics compared to baseline methods. 相似文献
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《Information processing & management》2020,57(1):102121
Text classification or categorization is the process of automatically tagging a textual document with most relevant labels or categories. When the number of labels is restricted to one, the task becomes single-label text categorization. However, the multi-label version is challenging. For Arabic language, both tasks (especially the latter one) become more challenging in the absence of large and free Arabic rich and rational datasets. Therefore, we introduce new rich and unbiased datasets for both the single-label (SANAD) as well as the multi-label (NADiA) Arabic text categorization tasks. Both corpora are made freely available to the research community on Arabic computational linguistics. Further, we present an extensive comparison of several deep learning (DL) models for Arabic text categorization in order to evaluate the effectiveness of such models on SANAD and NADiA. A unique characteristic of our proposed work, when compared to existing ones, is that it does not require a pre-processing phase and fully based on deep learning models. Besides, we studied the impact of utilizing word2vec embedding models to improve the performance of the classification tasks. Our experimental results showed solid performance of all models on SANAD corpus with a minimum accuracy of 91.18%, achieved by convolutional-GRU, and top performance of 96.94%, achieved by attention-GRU. As for NADiA, attention-GRU achieved the highest overall accuracy of 88.68% for a maximum subsets of 10 categories on “Masrawy” dataset. 相似文献
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《Information processing & management》2023,60(3):103298
Effective learning schemes such as fine-tuning, zero-shot, and few-shot learning, have been widely used to obtain considerable performance with only a handful of annotated training data. In this paper, we presented a unified benchmark to facilitate the problem of zero-shot text classification in Turkish. For this purpose, we evaluated three methods, namely, Natural Language Inference, Next Sentence Prediction and our proposed model that is based on Masked Language Modeling and pre-trained word embeddings on nine Turkish datasets for three main categories: topic, sentiment, and emotion. We used pre-trained Turkish monolingual and multilingual transformer models which can be listed as BERT, ConvBERT, DistilBERT and mBERT. The results showed that ConvBERT with the NLI method yields the best results with 79% and outperforms previously used multilingual XLM-RoBERTa model by 19.6%. The study contributes to the literature using different and unattempted transformer models for Turkish and showing improvement of zero-shot text classification performance for monolingual models over multilingual models. 相似文献
20.
一种基于k最近邻的快速文本分类方法 总被引：5，自引：0，他引：5
k最近邻方法是一种简单而有效的文本分类方法,但是传统的k最近邻分类方法在搜索k个最近邻时需要高强度的相似性计算,尤其是在训练集数据量很大情况下,全局的最优搜索几乎是不可能的.因此,加速k个最近邻的搜索是k最近邻方法实用的关键.本文提出了一种基于k最近邻的快速文本分类方法,它能够保证在海量数据集中进行快速有效的分类.实验结果表明这一方法较传统方法性能有显著提升. 相似文献