Open-world Multi-label Text Classification with Extremely Weak Supervision

• URL: http://arxiv.org/abs/2407.05609v1
• Date: Mon, 8 Jul 2024 04:52:49 GMT
• Title: Open-world Multi-label Text Classification with Extremely Weak Supervision
• Authors: Xintong Li, Jinya Jiang, Ria Dharmani, Jayanth Srinivasa, Gaowen Liu, Jingbo Shang,
• Abstract summary: We study open-world multi-label text classification under extremely weak supervision (XWS) We first utilize the user description to prompt a large language model (LLM) for dominant keyphrases of a subset of raw documents, and then construct a label space via clustering. We then apply a zero-shot multi-label classifier to locate the documents with small top predicted scores, so we can revisit their dominant keyphrases for more long-tail labels. X-MLClass exhibits a remarkable increase in ground-truth label space coverage on various datasets.
• Score: 30.85235057480158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study open-world multi-label text classification under extremely weak supervision (XWS), where the user only provides a brief description for classification objectives without any labels or ground-truth label space. Similar single-label XWS settings have been explored recently, however, these methods cannot be easily adapted for multi-label. We observe that (1) most documents have a dominant class covering the majority of content and (2) long-tail labels would appear in some documents as a dominant class. Therefore, we first utilize the user description to prompt a large language model (LLM) for dominant keyphrases of a subset of raw documents, and then construct a (initial) label space via clustering. We further apply a zero-shot multi-label classifier to locate the documents with small top predicted scores, so we can revisit their dominant keyphrases for more long-tail labels. We iterate this process to discover a comprehensive label space and construct a multi-label classifier as a novel method, X-MLClass. X-MLClass exhibits a remarkable increase in ground-truth label space coverage on various datasets, for example, a 40% improvement on the AAPD dataset over topic modeling and keyword extraction methods. Moreover, X-MLClass achieves the best end-to-end multi-label classification accuracy.

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