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1.
In text categorization, it is quite often that the numbers of documents in different categories are different, i.e., the class distribution is imbalanced. We propose a unique approach to improve text categorization under class imbalance by exploiting the semantic context in text documents. Specifically, we generate new samples of rare classes (categories with relatively small amount of training data) by using global semantic information of classes represented by probabilistic topic models. In this way, the numbers of samples in different categories can become more balanced and the performance of text categorization can be improved using this transformed data set. Indeed, the proposed method is different from traditional re-sampling methods, which try to balance the number of documents in different classes by re-sampling the documents in rare classes. Such re-sampling methods can cause overfitting. Another benefit of our approach is the effective handling of noisy samples. Since all the new samples are generated by topic models, the impact of noisy samples is dramatically reduced. Finally, as demonstrated by the experimental results, the proposed methods can achieve better performance under class imbalance and is more tolerant to noisy samples. 相似文献
2.
【目的/意义】旨在研究少量标注样本构建古文断句模型,减少在模型训练过程中样本标注所需的成本,为
探索数字技术与人文学科的融合发展提供崭新的思路。【方法/过程】从古文样本的不确定性和多样性出发,提出一
种加权多策略选样方法,有效结合了 BERT-BiLSTM-CRF、BERT-CRF等古文断句模型。通过引入信息熵和相
似性等概念,深入分析古籍文本的不确定性和多样性,运用加权计算评估古文样本对模型训练的价值高低,对加权
多策略方法所筛选的有价值样本进行人工标注,同时更新到训练集进行模型迭代训练。【结果/结论】以古籍《宋史》
为例进行研究,所提出的方法分别在 BERT-BiLSTM-CRF、BERT-CRF等古文断句模型训练过程中减少原来训
练样本量的50%、55%,进一步验证了方法的有效性。【创新/局限】加权多策略选样的方法为古文断句模型训练提供
了一种新思路,未来将探索该方法在古籍整理中其他任务的适用性。 相似文献
3.
Guoqiu Wen Xianxian Li Yonghua Zhu Linjun Chen Qimin Luo Malong Tan 《Information processing & management》2021,58(1):102388
The class distribution of imbalanced data sets is skewed in practical application. As traditional clustering methods mainly are designed for improving the overall learning performance, the majority class usually tends to be clustered and the minority class which is more valuable maybe ignored. Moreover, existing clustering methods can be limited for the performance of imbalanced and high-dimensional domains. In this paper, we present one-step spectral rotation clustering for imbalanced high-dimensional data (OSRCIH) by integrating self-paced learning and spectral rotation clustering in a unified learning framework, where sample selection and dimensionality reduction are simultaneously considered with mutual and iterative update. Specifically, the imbalance problem is considered by selecting the same number of training samples from each intrinsic group of the training data, where the sample-weight vector is obtained by self-paced learning. Moreover, dimensionality reduction is conducted by combining subspace learning and feature selection. Experimental analysis on synthetic datasets and real datasets showed that OSRCIH could recognize and enhance the weight of important samples and features so as to avoid the clustering method in favor of the majority class and to improve effectively the clustering performance. 相似文献
4.
《Information processing & management》2023,60(2):103235
Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models. 相似文献
5.
《Information processing & management》2016,52(4):491-512
This investigation deals with the problem of language identification of noisy texts, which could represent the primary step of many natural language processing or information retrieval tasks. Language identification is the task of automatically identifying the language of a given text. Although there exists several methods in the literature, their performances are not so convincing in practice.In this contribution, we propose two statistical approaches: the high frequency approach and the nearest prototype approach. In the first one, 5 algorithms of language identification are proposed and implemented, namely: character based identification (CBA), word based identification (WBA), special characters based identification (SCA), sequential hybrid algorithm (HA1) and parallel hybrid algorithm (HA2). In the second one, we use 11 similarity measures combined with several types of character N-Grams.For the evaluation task, the proposed methods are tested on forum datasets containing 32 different languages. Furthermore, an experimental comparison is made between the proposed approaches and some referential language identification tools such as: LIGA, NTC, Google translate and Microsoft Word. Results show that the proposed approaches are interesting and outperform the baseline methods of language identification on forum texts. 相似文献
6.
Extraction of pattern class associated discriminative subspace is critical to many pattern classification problems. Traditionally, pattern class labels are regarded as indicators to discriminate between pattern classes. In this work, a novel indicator model is proposed to extract discriminant subspace by projecting samples onto a space where the projected categories are mutually orthogonal and in-category normalized. Category orthonormal property and its connections to discriminative subspace extraction are derived. It is shown that the proposed method has a strong connection with the existing Fukunaga-Koontz Transformation but extends the category number from two to multiple. For applications with a large dimension size but limited number of samples, an analytic least-norm solver is developed for calculating the projection function. A discriminative subspace extraction method for multiple classes is proposed and is evaluated by a combination with classifiers. Experiments demonstrate a promising result of using the extracted category orthonormal subspace for multi-class subspace extraction when sample number is small. 相似文献
7.
《Information processing & management》2002,38(2):157-171
In every text, some words have frequency appearance and are considered as keywords because they have a strong relationship with the subjects of their texts, these words' frequencies change with time-series variation in a given period. However, in traditional text dealing methods and text search techniques, the importance of frequency change with time-series variation is not considered. Therefore, traditional methods could not correctly determine the index of word's popularity in a given period. In this paper, a new method is proposed to estimate automatically the stability classes (increasing, relatively constant, and decreasing) that indicate word's popularity with time-series variation based on the frequency change in past text data. At first, learning data were produced by defining five attributes to measure the frequency change of a word quantitatively. These five attributes were extracted automatically from electronic texts. These learning data were manual (human) classified into three stability classes. Then, these data were subjected to a decision tree to determine automatically stability classes of analysis data (test data). For learning data, we obtained the attribute values of 443 proper nouns that were extracted from 2216 articles of CNN newspapers (1997–1999) that discussed professional baseball. For testing data, 472 proper nouns that were extracted from 972 articles of CNN newspaper (1997–2000) then classified them automatically using decision tree. According to the comparison between the evaluation of the decision tree results and manually (human) results, F-measures of increasing, relatively constant and decreasing classes were 0.847, 0.851, and 0.768, respectively, and the effectiveness of this method is achieved. 相似文献
8.
Text categorization is an important research area and has been receiving much attention due to the growth of the on-line information and of Internet. Automated text categorization is generally cast as a multi-class classification problem. Much of previous work focused on binary document classification problems. Support vector machines (SVMs) excel in binary classification, but the elegant theory behind large-margin hyperplane cannot be easily extended to multi-class text classification. In addition, the training time and scaling are also important concerns. On the other hand, other techniques naturally extensible to handle multi-class classification are generally not as accurate as SVM. This paper presents a simple and efficient solution to multi-class text categorization. Classification problems are first formulated as optimization via discriminant analysis. Text categorization is then cast as the problem of finding coordinate transformations that reflects the inherent similarity from the data. While most of the previous approaches decompose a multi-class classification problem into multiple independent binary classification tasks, the proposed approach enables direct multi-class classification. By using generalized singular value decomposition (GSVD), a coordinate transformation that reflects the inherent class structure indicated by the generalized singular values is identified. Extensive experiments demonstrate the efficiency and effectiveness of the proposed approach. 相似文献
9.
《Information processing & management》2016,52(6):1004-1017
The rapid growth of documents in different languages, the increased accessibility of electronic documents, and the availability of translation tools have caused cross-lingual plagiarism detection research area to receive increasing attention in recent years. The task of cross-language plagiarism detection entails two main steps: candidate retrieval and assessing pairwise document similarity. In this paper we examine candidate retrieval, where the goal is to find potential source documents of a suspicious text. Our proposed method for cross-language plagiarism detection is a keyword-focused approach. Since plagiarism usually happens in parts of the text, there is a requirement to segment the texts into fragments to detect local similarity. Therefore we propose a topic-based segmentation algorithm to convert the suspicious document to a set of related passages. After that, we use a proximity-based model to retrieve documents with the best matching passages. Experiments show promising results for this important phase of cross-language plagiarism detection. 相似文献
10.
《Information processing & management》2016,52(5):873-884
OCR errors in text harm information retrieval performance. Much research has been reported on modelling and correction of Optical Character Recognition (OCR) errors. Most of the prior work employ language dependent resources or training texts in studying the nature of errors. However, not much research has been reported that focuses on improving retrieval performance from erroneous text in the absence of training data. We propose a novel approach for detecting OCR errors and improving retrieval performance from the erroneous corpus in a situation where training samples are not available to model errors. In this paper we propose a method that automatically identifies erroneous term variants in the noisy corpus, which are used for query expansion, in the absence of clean text. We employ an effective combination of contextual information and string matching techniques. Our proposed approach automatically identifies the erroneous variants of query terms and consequently leads to improvement in retrieval performance through query expansion. Our proposed approach does not use any training data or any language specific resources like thesaurus for identification of error variants. It also does not expend any knowledge about the language except that the word delimiter is blank space. We have tested our approach on erroneous Bangla (Bengali in English) and Hindi FIRE collections, and also on TREC Legal IIT CDIP and TREC 5 Confusion track English corpora. Our proposed approach has achieved statistically significant improvements over the state-of-the-art baselines on most of the datasets. 相似文献
11.
Learning from imbalanced datasets is difficult. The insufficient information that is associated with the minority class impedes making a clear understanding of the inherent structure of the dataset. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced, because they aim to optimize the overall accuracy without considering the relative distribution of each class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs may suffer from biased decision boundaries, and that their prediction performance drops dramatically when the data is highly skewed. We propose to combine an integrated sampling technique, which incorporates both over-sampling and under-sampling, with an ensemble of SVMs to improve the prediction performance. Extensive experiments show that our method outperforms individual SVMs as well as several other state-of-the-art classifiers. 相似文献
12.
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》2023,60(3):103261
Cross-genre author profiling aims to build generalized models for predicting profile traits of authors that can be helpful across different text genres for computer forensics, marketing, and other applications. The cross-genre author profiling task becomes challenging when dealing with low-resourced languages due to the lack of availability of standard corpora and methods. The task becomes even more challenging when the data is code-switched, which is informal and unstructured. In previous studies, the problem of cross-genre author profiling has been mainly explored for mono-lingual texts in highly resourced languages (English, Spanish, etc.). However, it has not been thoroughly explored for the code-switched text which is widely used for communication over social media. To fulfill this gap, we propose a transfer learning-based solution for the cross-genre author profiling task on code-switched (English–RomanUrdu) text using three widely known genres, Facebook comments/posts, Tweets, and SMS messages. In this article, firstly, we experimented with the traditional machine learning, deep learning and pre-trained transfer learning models (MBERT, XLMRoBERTa, ULMFiT, and XLNET) for the same-genre and cross-genre gender identification task. We then propose a novel Trans-Switch approach that focuses on the code-switching nature of the text and trains on specialized language models. In addition, we developed three RomanUrdu to English translated corpora to study the impact of translation on author profiling tasks. The results show that the proposed Trans-Switch model outperforms the baseline deep learning and pre-trained transfer learning models for cross-genre author profiling task on code-switched text. Further, the experimentation also shows that the translation of RomanUrdu text does not improve results. 相似文献
15.
《Information processing & management》1999,35(2):181-191
The automatic text summary concerns the language industries. This work proposes a system automatically and directly transforming a source text into a reduced target text. The system deals exclusively with scientific and technical texts. It is based on the identification of specific expressions allowing an evaluation of the relevance of the sentence concerned, which can then be selected for the elaboration of the summary. The procedure consists in attributing a score to each sentence of the text and then eliminating those having the lowest scores. To produce the RAFI system (automatic summary based on indicative fragments), we resorted to the linguistic means of discourse analysis and the computing capacity of data processing instruments. This system could be adapted to Internet. 相似文献
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17.
Document classification, with the blooming of the Internet information delivery, has become indispensable required and is expected to be disposed by an automatic text categorization. This paper presents a text categorization system to solve the multi-class categorization problem. The system consists of two modules: the processing module and the classifying module. In the first module, ICF and Uni are used as the indictors to extract the relevant terms. While the fuzzy set theory is incorporated into the OAA-SVM in the classifying module, we specifically propose an OAA-FSVM classifier to implement a multi-class classification system. The performances of OAA-SVM and OAA-FSVM are evaluated by macro-average performance index. 相似文献
18.
《Information processing & management》2020,57(6):102264
Searching the Internet for a certain topic can become a daunting task because users cannot read and comprehend all the resulting texts. Automatic Text summarization (ATS) in this case is clearly beneficial because manual summarization is expensive and time-consuming. To enhance ATS for single documents, this paper proposes a novel extractive graph-based framework “EdgeSumm” that relies on four proposed algorithms. The first algorithm constructs a new text graph model representation from the input document. The second and third algorithms search the constructed text graph for sentences to be included in the candidate summary. When the resulting candidate summary still exceeds a user-required limit, the fourth algorithm is used to select the most important sentences. EdgeSumm combines a set of extractive ATS methods (namely graph-based, statistical-based, semantic-based, and centrality-based methods) to benefit from their advantages and overcome their individual drawbacks. EdgeSumm is general for any document genre (not limited to a specific domain) and unsupervised so it does not require any training data. The standard datasets DUC2001 and DUC2002 are used to evaluate EdgeSumm using the widely used automatic evaluation tool: Recall-Oriented Understudy for Gisting Evaluation (ROUGE). EdgeSumm gets the highest ROUGE scores on DUC2001. For DUC2002, the evaluation results show that the proposed framework outperforms the state-of-the-art ATS systems by achieving improvements of 1.2% and 4.7% over the highest scores in the literature for the metrics of ROUGE-1 and ROUGE-L respectively. In addition, EdgeSumm achieves very competitive results for the metrics of ROUGE-2 and ROUGE-SU4. 相似文献
19.
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
20.
《Information processing & management》2016,52(3):359-373
The purpose of this article is to validate, through two empirical studies, a new method for automatic evaluation of written texts, called Inbuilt Rubric, based on the Latent Semantic Analysis (LSA) technique, which constitutes an innovative and distinct turn with respect to LSA application so far. In the first empirical study, evidence of the validity of the method to identify and evaluate the conceptual axes of a text in a sample of 78 summaries by secondary school students is sought. Results show that the proposed method has a significantly higher degree of reliability than classic LSA methods of text evaluation, and displays very high sensitivity to identify which conceptual axes are included or not in each summary. A second study evaluates the method's capacity to interact and provide feedback about quality in a real online system on a sample of 924 discursive texts written by university students. Results show that students improved the quality of their written texts using this system, and also rated the experience very highly. The final conclusion is that this new method opens a very interesting way regarding the role of automatic assessors in the identification of presence/absence and quality of elaboration of relevant conceptual information in texts written by students with lower time costs than the usual LSA-based methods. 相似文献