Work

FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification

Year: 2022

Type: conference-paper

Abstract: Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data.Recent research shows that keyword-driven methods can achieve state-ofthe-art performance on various tasks.However, these methods not only rely on carefullycrafted class descriptions to obtain classspecific keywords but also require substantial amount of unlabeled data and takes a long time to train.This paper proposes FastClass, an efficient weakly-supervised classification approach.It uses dense text representation to retrieve class-relevant documents from external unlabeled corpus and selects an optimal subset to train a classifier.Compared to keyworddriven methods, our approach is less reliant on initial class descriptions as it no longer needs to expand each class description into a set of class-specific keywords.Experiments on a wide range of classification tasks show that the proposed approach frequently outperforms keyword-driven models in terms of classification accuracy and often enjoys orders-ofmagnitude faster training speed.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/2022.emnlp-main.313

Authors: Tingyu Xia, Yue Wang, Yuan Tian, Yi Chang

Institutions: Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, University of North Carolina at Chapel Hill

Language: English

Topic: Text and Document Classification Technologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, People's Government of Jilin Province, Fundamental Research Funds for the Central Universities

Awards: G3189195171, G524871825, G7138386155