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"Text Classification": models, code, and papers

PTR: Prompt Tuning with Rules for Text Classification

May 31, 2021
Xu Han, Weilin Zhao, Ning Ding, Zhiyuan Liu, Maosong Sun

Fine-tuned pre-trained language models (PLMs) have achieved awesome performance on almost all NLP tasks. By using additional prompts to fine-tune PLMs, we can further stimulate the rich knowledge distributed in PLMs to better serve downstream task. Prompt tuning has achieved promising results on some few-class classification tasks such as sentiment classification and natural language inference. However, manually designing lots of language prompts is cumbersome and fallible. For those auto-generated prompts, it is also expensive and time-consuming to verify their effectiveness in non-few-shot scenarios. Hence, it is challenging for prompt tuning to address many-class classification tasks. To this end, we propose prompt tuning with rules (PTR) for many-class text classification, and apply logic rules to construct prompts with several sub-prompts. In this way, PTR is able to encode prior knowledge of each class into prompt tuning. We conduct experiments on relation classification, a typical many-class classification task, and the results on benchmarks show that PTR can significantly and consistently outperform existing state-of-the-art baselines. This indicates that PTR is a promising approach to take advantage of PLMs for those complicated classification tasks.

  

FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications

Jan 31, 2020
Dou Goodman, Lv Zhonghou, Wang minghua

In this paper, we present a novel algorithm, FastWordBug, to efficiently generate small text perturbations in a black-box setting that forces a sentiment analysis or text classification mode to make an incorrect prediction. By combining the part of speech attributes of words, we propose a scoring method that can quickly identify important words that affect text classification. We evaluate FastWordBug on three real-world text datasets and two state-of-the-art machine learning models under black-box setting. The results show that our method can significantly reduce the accuracy of the model, and at the same time, we can call the model as little as possible, with the highest attack efficiency. We also attack two popular real-world cloud services of NLP, and the results show that our method works as well.

  

Document classification methods

Sep 16, 2019
Madjid Khalilian, Shiva Hassanzadeh

Information on different fields which are collected by users requires appropriate management and organization to be structured in a standard way and retrieved fast and more easily. Document classification is a conventional method to separate text based on their subjects among scientific text, web pages and digital library. Different methods and techniques are proposed for document classifications that have advantages and deficiencies. In this paper, several unsupervised and supervised document classification methods are studied and compared.

  

Character-level Convolutional Network for Text Classification Applied to Chinese Corpus

Nov 15, 2016
Weijie Huang, Jun Wang

This article provides an interesting exploration of character-level convolutional neural network solving Chinese corpus text classification problem. We constructed a large-scale Chinese language dataset, and the result shows that character-level convolutional neural network works better on Chinese corpus than its corresponding pinyin format dataset. This is the first time that character-level convolutional neural network applied to text classification problem.

* MSc Thesis, 44 pages 
  

Classification Analysis Of Authorship Fiction Texts in The Space Of Semantic Fields

Oct 22, 2012
Bohdan Pavlyshenko

The use of naive Bayesian classifier (NB) and the classifier by the k nearest neighbors (kNN) in classification semantic analysis of authors' texts of English fiction has been analysed. The authors' works are considered in the vector space the basis of which is formed by the frequency characteristics of semantic fields of nouns and verbs. Highly precise classification of authors' texts in the vector space of semantic fields indicates about the presence of particular spheres of author's idiolect in this space which characterizes the individual author's style.

* 6 pages, 2 figures 
  

Distant finetuning with discourse relations for stance classification

Apr 27, 2022
Lifeng Jin, Kun Xu, Linfeng Song, Dong Yu

Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with individual debate topics. In this paper, in order to train a system independent from topics, we propose a new method to extract data with silver labels from raw text to finetune a model for stance classification. The extraction relies on specific discourse relation information, which is shown as a reliable and accurate source for providing stance information. We also propose a 3-stage training framework where the noisy level in the data used for finetuning decreases over different stages going from the most noisy to the least noisy. Detailed experiments show that the automatically annotated dataset as well as the 3-stage training help improve model performance in stance classification. Our approach ranks 1st among 26 competing teams in the stance classification track of the NLPCC 2021 shared task Argumentative Text Understanding for AI Debater, which confirms the effectiveness of our approach.

* NLPCC 2021 
  

Deep Health Care Text Classification

Oct 23, 2017
Vinayakumar R, Barathi Ganesh HB, Anand Kumar M, Soman KP

Health related social media mining is a valuable apparatus for the early recognition of the diverse antagonistic medicinal conditions. Mostly, the existing methods are based on machine learning with knowledge-based learning. This working note presents the Recurrent neural network (RNN) and Long short-term memory (LSTM) based embedding for automatic health text classification in the social media mining. For each task, two systems are built and that classify the tweet at the tweet level. RNN and LSTM are used for extracting features and non-linear activation function at the last layer facilitates to distinguish the tweets of different categories. The experiments are conducted on 2nd Social Media Mining for Health Applications Shared Task at AMIA 2017. The experiment results are considerable; however the proposed method is appropriate for the health text classification. This is primarily due to the reason that, it doesn't rely on any feature engineering mechanisms.

* 4 pages 
  

Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions

Jul 07, 2022
Kosuke Nishida, Kyosuke Nishida, Shuichi Nishioka

Humans can obtain the knowledge of novel visual concepts from language descriptions, and we thus use the few-shot image classification task to investigate whether a machine learning model can have this capability. Our proposed model, LIDE (Learning from Image and DEscription), has a text decoder to generate the descriptions and a text encoder to obtain the text representations of machine- or user-generated descriptions. We confirmed that LIDE with machine-generated descriptions outperformed baseline models. Moreover, the performance was improved further with high-quality user-generated descriptions. The generated descriptions can be viewed as the explanations of the model's predictions, and we observed that such explanations were consistent with prediction results. We also investigated why the language description improved the few-shot image classification performance by comparing the image representations and the text representations in the feature spaces.

* Findings of NAACL2022 
  

LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network

Sep 23, 2020
Xinyi Zhang, Jiahao Xu, Charlie Soh, Lihui Chen

Hierarchical multi-label text classification(HMTC) problems become popular recently because of its practicality. Most existing algorithms for HMTC focus on the design of classifiers, and are largely referred to as local, global, or a combination of local/global approaches. However, a few studies have started exploring hierarchical feature extraction based on the label hierarchy associating with text in HMTC. In this paper, a \textbf{N}eural network-based method called \textbf{LA-HCN} is proposed where a novel \textbf{L}abel-based \textbf{A}ttention module is designed to hierarchically extract important information from the text based on different labels. Besides, local and global document embeddings are separately generated to support the respective local and global classifications. In our experiments, LA-HCN achieves the top performance on the four public HMTC datasets when compared with other neural network-based state-of-the-art algorithms. The comparison between LA-HCN with its variants also demonstrates the effectiveness of the proposed label-based attention module as well as the use of the combination of local and global classifications. By visualizing the learned attention(words), we find LA-HCN is able to extract meaningful but different information from text based on different labels which is helpful for human understanding and explanation of classification results.

  
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