T axo C lass: Hierarchical Multi-Label Text Classification Using Only Class Names (2021)

Abstract: Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of classes from a taxonomic class hierarchy. Most existing HMTC methods train classifiers using massive human-labeled documents, which are often too costly to obtain in real-world applications. In this paper, we explore to conduct HMTC based on only class surface names as supervision signals. We observe that to perform HMTC, human experts typically first pinpoint a few most essential classes for the document as its “core classes”, and then check core classes’ ancestor classes to ensure the coverage. To mimic human experts, we propose a novel HMTC framework, named TaxoClass. Specifically, TaxoClass (1) calculates document-class similarities using a textual entailment model, (2) identifies a document’s core classes and utilizes confident core classes to train a taxonomy-enhanced classifier, and (3) generalizes the classifier via multi-label self-training. Our experiments on two challenging datasets show TaxoClass can achieve around 0.71 Example-F1 using only class names, outperforming the best previous method by 25%.

## introduction hierarchical multi-label text classification (hmtc) aims to assign each text document to a set of relevant classes from a class taxonomy. as a fundamental task in nlp, hmtc has many applications such as product categorization @xcite , semantic indexing @xcite , and finegrained entity typing @xcite . most existing methods address hmtc in a supervised fashion -they first ask humans to provide many labeled documents and then train a text classifier for prediction. many classifiers have been developed with different deep learning architectures such as cnn @xcite , rnn @xcite , attention network @xcite , and achieved decent performance when trained on massive human-labeled documents. despite such a document: when our son was about 4 months old, our doctor said we could give him crafted cereal. we bought this product and put it in his bottle. he loved this stuff! this cereal digests well and didn't lock up his bowels at all. we highly recommend this cereal. success, people find that applying these methods to many real-world scenarios remains challenging as the human labeling process is often too timeconsuming and expensive. recently, more studies have been developed to address text classification using smaller amount of labeled data. first, several semi-supervised methods @xcite propose to use abundant unlabeled documents to assist model training on labeled dataset. although mitigating the human annotation burden, these methods still require a labeled dataset that covers all classes, which could be too expensive to obtain when we have a large number of classes in hmtc. second, some weakly-supervised models exploit class indicative keywords @xcite @xcite or class surface names @xcite to derive pseudo-labeled data for model training. nevertheless, these models all assume each document has only one class and all class surface names (or class indicative keywords) must appear in the corpus, which are too restrictive for hmtc. in this paper, we study the problem of weaklysupervised hierarchical multi-label text classification where only class surface names, a class taxonomy, and an unlabeled corpus are available for model training. this setting is closer to how humans resolve the hmtc problem -we perform classification by understanding each class from its surface name rather than learning from labeled documents. we observe that when asked to assign multiple classes to a document, humans will first pinpoint most essential "core classes" and then check whether their ancestor classes in the taxonomy should also be tagged. taking the document in fig. 1 as an example, humans can quickly identify this review text is clearly about "baby cereal" and "crafted cereal", which are the core classes. after assigning these two most essential classes to the document, people continue to check the core classes' ancestor classes and find "feeding" as well as "baby food" should be tagged. motivated by the above human labeling process, we propose taxoclass, a weakly-supervised hmtc framework including four major steps. first, we calculate the document-class similarity using a pre-trained textual entailment model @xcite . second, we identify each document's core classes by (1) selecting candidate core classes that are most similar to the document at each level in a top-down fashion, and (2) choosing document, candidate core class pairs that are salient across the whole unlabeled corpus. third, we derive training data from document core classes and use them to train a text classifier. this classifier includes a document encoder based on pre-trained bert @xcite , a class encoder capturing class taxonomy structure, and a text matching network computing the probability of a document being tagged with each class. finally, we generalize this text classifier using multi-label self-training on all unlabeled documents. contributions. to summarize, our major contributions are as follows: (1) we propose a weaklysupervised framework taxoclass that only requires class surface names to perform hierarchical multilabel text classification. to the best of our knowledge, taxoclass is the first weakly-supervised hmtc method. (2) we develop an unsupervised method to identify document core classes based on which a text classifier can be learned. (3) we conduct extensive experiments to verify the effectiveness of taxoclass on two real-world datasets. ## problem formulation in this section, we introduce the notations and present our task definition. notations. a corpus @xmath0, . . . , d n } is a text collection where each document d @xmath1. a class taxonomy @xmath2, r) is a directed acyclic graph where each node represents a class c j and each directed edge c m , c @xmath3. in this work, we assume each class c j has a surface name s j (either a word or a phrase) that serves as the weak supervision signal. ## our taxoclass framework our taxoclass framework consists of four major steps: (1) document-class similarity calculation, (2) document core class mining, (3) core class guided classifier training, and (4) multi-label self-training. fig. 2 shows our framework overview and below sections discuss each step in more details. ## conclusions & future work this paper studies the hierarchical multi-label text classification problem when only class surface names, instead of massive labeled documents, are given. we propose a novel taxoclass framework which leverages the class taxonomy structure to derive document core classes and learns taxonomyenhanced text classifier for prediction. extensive experiments demonstrate the effectiveness of taxoclass on two real-world datasets from different domains. in the future, we plan to explore how taxoclass framework can be integrated with semi-supervised methods and data augmentation methods, when some class surface names are too ambiguous to indicate class semantics. moreover, we consider extending our multi-label self-training method to other related nlp tasks such as finegrained entity typing.