More Like this

1. Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks

   https://doi.org/10.1145/3442381.3450114  

   Zhang, Xinyang ; Zhang, Chenwei ; Dong, Xin Luna ; Shang, Jingbo ; Han, Jiawei ( April 2021 , WWW '21: The Web Conference 2021)

   Text categorization is an essential task in Web content analysis. Considering the ever-evolving Web data and new emerging categories, instead of the laborious supervised setting, in this paper, we focus on the minimally-supervised setting that aims to categorize documents effectively, with a couple of seed documents annotated per category. We recognize that texts collected from the Web are often structure-rich, i.e., accompanied by various metadata. One can easily organize the corpus into a text-rich network, joining raw text documents with document attributes, high-quality phrases, label surface names as nodes, and their associations as edges. Such a network provides a holistic view of the corpus’ heterogeneous data sources and enables a joint optimization for network-based analysis and deep textual model training. We therefore propose a novel framework for minimally supervised categorization by learning from the text-rich network. Specifically, we jointly train two modules with different inductive biases – a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning. Each module generates pseudo training labels from the unlabeled document set, and both modules mutually enhance each other by co-training using pooled pseudo labels. We test our model on two real-world datasets. On the challenging e-commercemore »product categorization dataset with 683 categories, our experiments show that given only three seed documents per category, our framework can achieve an accuracy of about 92%, significantly outperforming all compared methods; our accuracy is only less than 2% away from the supervised BERT model trained on about 50K labeled documents.« less
2. META: Metadata-Empowered Weak Supervision for Text Classification

   https://doi.org/10.18653/v1/2020.emnlp-main.670  

   Mekala, Dheeraj ; Zhang, Xinyang ; Shang, Jingbo ( January 2020 , Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   Recent advances in weakly supervised learn- ing enable training high-quality text classifiers by only providing a few user-provided seed words. Existing methods mainly use text data alone to generate pseudo-labels despite the fact that metadata information (e.g., author and timestamp) is widely available across various domains. Strong label indicators exist in the metadata and it has been long overlooked mainly due to the following challenges: (1) metadata is multi-typed, requiring systematic modeling of different types and their combinations, (2) metadata is noisy, some metadata entities (e.g., authors, venues) are more compelling label indicators than others. In this paper, we propose a novel framework, META, which goes beyond the existing paradigm and leverages metadata as an additional source of weak supervision. Specifically, we organize the text data and metadata together into a text-rich network and adopt network motifs to capture appropriate combinations of metadata. Based on seed words, we rank and filter motif instances to distill highly label-indicative ones as “seed motifs”, which provide additional weak supervision. Following a boot-strapping manner, we train the classifier and expand the seed words and seed motifs iteratively. Extensive experiments and case studies on real-world datasets demonstrate superior performance and significant advantages of leveraging metadatamore »as weak supervision.« less
3. Hierarchical Metadata-Aware Document Categorization under Weak Supervision

   https://doi.org/10.1145/3437963.3441730  

   Zhang, Yu ; Chen, Xiusi ; Meng, Yu ; Han, Jiawei ( March 2021 , WSDM'21, The Fourteenth ACM International Conference on Web Search and Data Mining, March 2021)

   Categorizing documents into a given label hierarchy is intuitively appealing due to the ubiquity of hierarchical topic structures in massive text corpora. Although related studies have achieved satisfying performance in fully supervised hierarchical document classification, they usually require massive human-annotated training data and only utilize text information. However, in many domains, (1) annotations are quite expensive where very few training samples can be acquired; (2) documents are accompanied by metadata information. Hence, this paper studies how to integrate the label hierarchy, metadata, and text signals for document categorization under weak supervision. We develop HiMeCat, an embedding-based generative framework for our task. Specifically, we propose a novel joint representation learning module that allows simultaneous modeling of category dependencies, metadata information and textual semantics, and we introduce a data augmentation module that hierarchically synthesizes training documents to complement the original, small-scale training set. Our experiments demonstrate a consistent improvement of HiMeCat over competitive baselines and validate the contribution of our representation learning and data augmentation modules.
4. Text Classification Using Label Names Only: A Language Model Self-Training Approach

   https://doi.org/10.18653/v1/2020.emnlp-main.724  

   Meng, Yu ; Zhang, Yunyi ; Huang, Jiaxin ; Xiong, Chenyan ; Ji, Heng ; Zhang, Chao ; Han, Jiawei ( January 2020 , EMNLP'20: 2020 Conf. on Empirical Methods in Natural Language Processing, Nov. 2020)

   Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Hu-mans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified. In this paper, we explore the potential of only using the label name of each class to train classification models on un-labeled data, without using any labeled documents. We use pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification. Our method (1) associates semantically related words with the label names, (2) finds category-indicative words and trains the model to predict their implied categories, and (3) generalizes the model via self-training. We show that our model achieves around 90% ac-curacy on four benchmark datasets including topic and sentiment classification without using any labeled documents but learning from unlabeled data supervised by at most 3 words (1 in most cases) per class as the label name1.
5. Weakly-Supervised Neural Text Classification

   https://doi.org/10.1145/3269206.3271737  

   Meng, Yu ; Shen, Jiaming ; Zhang, Chao ; Han, Jiawei ( October 2018 , Proceedings of the 27th {ACM} International Conference on Information and Knowledge Management, {CIKM} 2018)

   Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semisupervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.