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"Text Classification": models, code, and papers

Large Scale Legal Text Classification Using Transformer Models

Oct 24, 2020
Zein Shaheen, Gerhard Wohlgenannt, Erwin Filtz

Large multi-label text classification is a challenging Natural Language Processing (NLP) problem that is concerned with text classification for datasets with thousands of labels. We tackle this problem in the legal domain, where datasets, such as JRC-Acquis and EURLEX57K labeled with the EuroVoc vocabulary were created within the legal information systems of the European Union. The EuroVoc taxonomy includes around 7000 concepts. In this work, we study the performance of various recent transformer-based models in combination with strategies such as generative pretraining, gradual unfreezing and discriminative learning rates in order to reach competitive classification performance, and present new state-of-the-art results of 0.661 (F1) for JRC-Acquis and 0.754 for EURLEX57K. Furthermore, we quantify the impact of individual steps, such as language model fine-tuning or gradual unfreezing in an ablation study, and provide reference dataset splits created with an iterative stratification algorithm.


On the Importance of Attention in Meta-Learning for Few-Shot Text Classification

Jun 03, 2018
Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, Stan Matwin

Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding. Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification. The essential difference between MAML and ATAML is in the separation of task-agnostic representation learning and task-specific attentive adaptation. The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms. We provide evidence to show that the attention mechanism in ATAML has a synergistic effect on learning performance. In comparisons with models trained from random initialization, pretrained models and meta trained MAML, our proposed ATAML method generalizes better on single-label and multi-label classification tasks in miniRCV1 and miniReuters-21578 datasets.

* 13 pages, 4 figures, submitted to NIPS 

A Comparison of Synthetic Oversampling Methods for Multi-class Text Classification

Aug 11, 2020
Anna Glazkova

The authors compared oversampling methods for the problem of multi-class topic classification. The SMOTE algorithm underlies one of the most popular oversampling methods. It consists in choosing two examples of a minority class and generating a new example based on them. In the paper, the authors compared the basic SMOTE method with its two modifications (Borderline SMOTE and ADASYN) and random oversampling technique on the example of one of text classification tasks. The paper discusses the k-nearest neighbor algorithm, the support vector machine algorithm and three types of neural networks (feedforward network, long short-term memory (LSTM) and bidirectional LSTM). The authors combine these machine learning algorithms with different text representations and compared synthetic oversampling methods. In most cases, the use of oversampling techniques can significantly improve the quality of classification. The authors conclude that for this task, the quality of the KNN and SVM algorithms is more influenced by class imbalance than neural networks.

* 12 pages, 5 figures 

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

Apr 25, 2020
Jiaao Chen, Zichao Yang, Diyi Yang

This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. Moreover, we leverage recent advances in data augmentation to guess low-entropy labels for unlabeled data, hence making them as easy to use as labeled data.By mixing labeled, unlabeled and augmented data, MixText significantly outperformed current pre-trained and fined-tuned models and other state-of-the-art semi-supervised learning methods on several text classification benchmarks. The improvement is especially prominent when supervision is extremely limited. We have publicly released our code at

* ACL 2020 

Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification

Sep 06, 2019
Yichao Zhou, Jyun-Yu Jiang, Kai-Wei Chang, Wei Wang

Adversarial attacks against machine learning models have threatened various real-world applications such as spam filtering and sentiment analysis. In this paper, we propose a novel framework, learning to DIScriminate Perturbations (DISP), to identify and adjust malicious perturbations, thereby blocking adversarial attacks for text classification models. To identify adversarial attacks, a perturbation discriminator validates how likely a token in the text is perturbed and provides a set of potential perturbations. For each potential perturbation, an embedding estimator learns to restore the embedding of the original word based on the context and a replacement token is chosen based on approximate kNN search. DISP can block adversarial attacks for any NLP model without modifying the model structure or training procedure. Extensive experiments on two benchmark datasets demonstrate that DISP significantly outperforms baseline methods in blocking adversarial attacks for text classification. In addition, in-depth analysis shows the robustness of DISP across different situations.

* 10 pages, 8 tables, 4 figures 

Myers-Briggs personality classification from social media text using pre-trained language models

Jul 10, 2022
Vitor Garcia dos Santos, Ivandré Paraboni

In Natural Language Processing, the use of pre-trained language models has been shown to obtain state-of-the-art results in many downstream tasks such as sentiment analysis, author identification and others. In this work, we address the use of these methods for personality classification from text. Focusing on the Myers-Briggs (MBTI) personality model, we describe a series of experiments in which the well-known Bidirectional Encoder Representations from Transformers (BERT) model is fine-tuned to perform MBTI classification. Our main findings suggest that the current approach significantly outperforms well-known text classification models based on bag-of-words and static word embeddings alike across multiple evaluation scenarios, and generally outperforms previous work in the field.

* Journal of Universal Computer Science, vol. 28, no. 4 (2022), 378-395 
* 19 pages 

Research on Dual Channel News Headline Classification Based on ERNIE Pre-training Model

Feb 14, 2022
Junjie Li, Hui Cao

The classification of news headlines is an important direction in the field of NLP, and its data has the characteristics of compactness, uniqueness and various forms. Aiming at the problem that the traditional neural network model cannot adequately capture the underlying feature information of the data and cannot jointly extract key global features and deep local features, a dual-channel network model DC-EBAD based on the ERNIE pre-training model is proposed. Use ERNIE to extract the lexical, semantic and contextual feature information at the bottom of the text, generate dynamic word vector representations fused with context, and then use the BiLSTM-AT network channel to secondary extract the global features of the data and use the attention mechanism to give key parts higher The weight of the DPCNN channel is used to overcome the long-distance text dependence problem and obtain deep local features. The local and global feature vectors are spliced, and finally passed to the fully connected layer, and the final classification result is output through Softmax. The experimental results show that the proposed model improves the accuracy, precision and F1-score of news headline classification compared with the traditional neural network model and the single-channel model under the same conditions. It can be seen that it can perform well in the multi-classification application of news headline text under large data volume.


X-Class: Text Classification with Extremely Weak Supervision

Oct 24, 2020
Zihan Wang, Dheeraj Mekala, Jingbo Shang

In this paper, we explore to conduct text classification with extremely weak supervision, i.e., only relying on the surface text of class names. This is a more challenging setting than the seed-driven weak supervision, which allows a few seed words per class. We opt to attack this problem from a representation learning perspective -- ideal document representations should lead to very close results between clustering and the desired classification. In particular, one can classify the same corpus differently (e.g., based on topics and locations), so document representations must be adaptive to the given class names. We propose a novel framework X-Class to realize it. Specifically, we first estimate comprehensive class representations by incrementally adding the most similar word to each class until inconsistency appears. Following a tailored mixture of class attention mechanisms, we obtain the document representation via a weighted average of contextualized token representations. We then cluster and align the documents to classes with the prior of each document assigned to its nearest class. Finally, we pick the most confident documents from each cluster to train a text classifier. Extensive experiments demonstrate that X-Class can rival and even outperform seed-driven weakly supervised methods on 7 benchmark datasets.


tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification

Apr 07, 2019
Blaž Škrlj, Matej Martinc, Jan Kralj, Nada Lavrač, Senja Pollak

The use of background knowledge remains largely unexploited in many text classification tasks. In this work, we explore word taxonomies as means for constructing new semantic features, which may improve the performance and robustness of the learned classifiers. We propose tax2vec, a parallel algorithm for constructing taxonomy based features, and demonstrate its use on six short-text classification problems, including gender, age and personality type prediction, drug effectiveness and side effect prediction, and news topic prediction. The experimental results indicate that the interpretable features constructed using tax2vec can notably improve the performance of classifiers; the constructed features, in combination with fast, linear classifiers tested against strong baselines, such as hierarchical attention neural networks, achieved comparable or better classification results on short documents. Further, tax2vec can also serve for extraction of corpus-specific keywords. Finally, we investigated the semantic space of potential features where we observe a similarity with the well known Zipf's law.