A Sample Extension Method Based on Wikipedia and Its Application in Text Classification

Zhu, Wenhao; Liu, Yiting; Hu, Guannan; Ni, Jianyue; Lu, Zhiguo

doi:10.1007/s11277-018-5416-z

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…Using Sohu News and texts from Fudan University datasets, Zhu et al. ( 2018 ) outperformed the baseline method with 30% of sample expansion. Based on the expansion of 100 samples, WSE with Naive Bayes achieved the best result with an F-measure of 72.5% approximately in the Sohu dataset.…”

Section: Results Analysis Per Datasetsmentioning

confidence: 99%

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A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim to fill this gap and present an up-to-date review of SSL for text classification. We retrieve 1794 works from the last 5 years from IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Then, 157 articles were selected to be included in this review. We present the application domain, datasets, and languages employed in the works. The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. We aim to provide researchers and practitioners with an outline of the area as well as useful information for their current research.

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Section: Results Analysis Per Datasetsmentioning

confidence: 99%

“…In a Chinese text classification for news, Zhu et al. ( 2018 ) developed a method based on Wikipedia sample extension (WSE). A network graph was constructed with concepts and their links extracted from Wikipedia.…”

Section: Semi-supervised Learning For Text Classificationmentioning

confidence: 99%

A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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“…Naïve Bayes; Support Vector Machine [92] It uses the knowledge of Wikipedia to extend the training samples, which is realized by network graph construction. Naïve Bayes; Support Vector Machine; Random Forest [93] It introduces an attentive meta-learning method for task-agnostic representation and realizes fast adaption in different tasks, thus having the ability of learning shared representation across tasks.…”

Section: Author-studymentioning

confidence: 99%

Natural Language Processing: Emerging Neural Approaches and Applications

2022

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“…Contribution Basic Language Model/Classifier [91] It proposes a novel co-training algorithm which uses an ensemble of classifiers created in multiple training iterations, with labeled data and unlabeled data trained jointly and with no added computational complexity. Naïve Bayes; Support Vector Machine [92] It uses the knowledge of Wikipedia to extend the training samples, which is realized by network graph construction.…”

Section: Author-studymentioning

confidence: 99%

A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists' opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years. Appl. Sci. 2019, 9, 3300 2 of 27 the travel. In order to utilize this user-generated content properly and further to meet the needs of tourists and promote the tourism industry, we need to analyze and exploit tourists' needs and opinions, and then identify the problems of tourism services or destinations, which has become a new path for tourism development. Besides, as tourism needs become increasingly personalized, visitors begin to pursue self-likeness, self-worth, and diversified travel experiences, and they are no longer willing to endure delays or waits. How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36]. Moreover, while aiming to make computers understand human language, NLP has become the essential tool for text data analysis and is undergoing fast-pace growing based on the applications of deep learning in word embedding, syntax analysis, machine translation, and text understanding. Machine learning-based NLP techniques have been widely used in tourism text analysis, with superior results [19,25]. In addition, due to its high capability for extracting selective and invariant features from texts, and its independency of prior knowledge and linguistic resources, deep learning has been reported to achieve higher performance than other approaches on many NL...

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A Sample Extension Method Based on Wikipedia and Its Application in Text Classification

Cited by 5 publications

References 13 publications

A review of semi-supervised learning for text classification

A review of semi-supervised learning for text classification

Natural Language Processing: Emerging Neural Approaches and Applications

A Review of Text Corpus-Based Tourism Big Data Mining

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