Meta-learning of Text Classification Tasks

This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve Bayes has been evaluated to be a practical text classification algorithm due to its simple classification model, reasonable classification accuracy, and easy update of classification model. Thus, many researchers have a strong incentive to improve the Naïve Bayes by combining it with other meta-learning approaches such as EM (Expectation Maximization) and Boosting. The EM approach is to combine the Naïve Bayes with the EM algorithm and the Boosting approach is to use the Naïve Bayes as a base classifier in the AdaBoost algorithm. For both approaches, a special uncertainty measure fit for Naïve Bayes learning is used. In the Naïve Bayes learning framework, these approaches are expected to be practical solutions to the problem of lack of training documents in text classification systems.

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Explicit Interaction Model towards Text Classification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016359 ◽

2019 ◽

Vol 33 ◽

pp. 6359-6366 ◽

Cited By ~ 3

Author(s):

Cunxiao Du ◽

Zhaozheng Chen ◽

Fuli Feng ◽

Lei Zhu ◽

Tian Gan ◽

...

Keyword(s):

Language Processing ◽

Text Classification ◽

Deep Neural Networks ◽

Interaction Mechanism ◽

Interaction Model ◽

Classification Task ◽

Fine Grained ◽

Word Level ◽

Benchmark Datasets ◽

Classification Tasks

Text classification is one of the fundamental tasks in natural language processing. Recently, deep neural networks have achieved promising performance in the text classification task compared to shallow models. Despite of the significance of deep models, they ignore the fine-grained (matching signals between words and classes) classification clues since their classifications mainly rely on the text-level representations. To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task. In particular, we design a novel framework, EXplicit interAction Model (dubbed as EXAM), equipped with the interaction mechanism. We justified the proposed approach on several benchmark datasets including both multilabel and multi-class text classification tasks. Extensive experimental results demonstrate the superiority of the proposed method. As a byproduct, we have released the codes and parameter settings to facilitate other researches.

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Towards Robust Text Classification with Semantics-Aware Recurrent Neural Architecture

Machine Learning and Knowledge Extraction ◽

10.3390/make1020034 ◽

2019 ◽

Vol 1 (2) ◽

pp. 575-589 ◽

Cited By ~ 1

Author(s):

Blaž Škrlj ◽

Jan Kralj ◽

Nada Lavrač ◽

Senja Pollak

Keyword(s):

Text Mining ◽

Language Processing ◽

Text Classification ◽

Deep Neural Networks ◽

Semantic Knowledge ◽

Text Documents ◽

Neural Architecture ◽

Classification Tasks ◽

And Gender ◽

Semantic Resources

Deep neural networks are becoming ubiquitous in text mining and natural language processing, but semantic resources, such as taxonomies and ontologies, are yet to be fully exploited in a deep learning setting. This paper presents an efficient semantic text mining approach, which converts semantic information related to a given set of documents into a set of novel features that are used for learning. The proposed Semantics-aware Recurrent deep Neural Architecture (SRNA) enables the system to learn simultaneously from the semantic vectors and from the raw text documents. We test the effectiveness of the approach on three text classification tasks: news topic categorization, sentiment analysis and gender profiling. The experiments show that the proposed approach outperforms the approach without semantic knowledge, with highest accuracy gain (up to 10%) achieved on short document fragments.

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Comparison of SVM and Some Older Classification Algorithms in Text Classification Tasks

IFIP International Federation for Information Processing - Artificial Intelligence in Theory and Practice ◽

10.1007/978-0-387-34747-9_18 ◽

2006 ◽

pp. 169-178 ◽

Cited By ~ 54

Author(s):

Fabrice Colas ◽

Pavel Brazdil

Keyword(s):

Text Classification ◽

Classification Algorithms ◽

Classification Tasks

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The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification

10.3233/faia210317 ◽

2021 ◽

Author(s):

Benjamin Clavié ◽

Marc Alphonsus

Keyword(s):

Deep Learning ◽

Language Processing ◽

Text Classification ◽

Traditional Approach ◽

Error Reduction ◽

Support Vector ◽

Learning Models ◽

Legal Text ◽

Classification Tasks ◽

Legal Domain

We aim to highlight an interesting trend to contribute to the ongoing debate around advances within legal Natural Language Processing. Recently, the focus for most legal text classification tasks has shifted towards large pre-trained deep learning models such as BERT. In this paper, we show that a more traditional approach based on Support Vector Machine classifiers reaches competitive performance with deep learning models. We also highlight that error reduction obtained by using specialised BERT-based models over baselines is noticeably smaller in the legal domain when compared to general language tasks. We discuss some hypotheses for these results to support future discussions.

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A comparative review on deep learning models for text classification

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v19.i1.pp325-335 ◽

2020 ◽

Vol 19 (1) ◽

pp. 325

Author(s):

Muhammad Zulqarnain ◽

Rozaida Ghazali ◽

Yana Mazwin Mohmad Hassim ◽

Muhammad Rehan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Language Processing ◽

Text Classification ◽

Question Answering ◽

Learning Models ◽

Semantic Classification ◽

Analysis Question ◽

Comparative Review ◽

Classification Tasks

<p>Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.</p>

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