Iterative Data Programming for Expanding Text Classification Corpora
Autor: | Ugrani Rajendra G, Neil Mallinar, Ayush Gupta, Tin Kam Ho, Abhishek Shah |
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Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language Computer science business.industry General Medicine Machine learning computer.software_genre Ensemble learning Task (project management) Machine Learning (cs.LG) Data set ComputingMethodologies_PATTERNRECOGNITION Simple (abstract algebra) Programming paradigm Artificial intelligence business computer Computation and Language (cs.CL) Sentence |
Zdroj: | AAAI |
DOI: | 10.48550/arxiv.2002.01412 |
Popis: | Real-world text classification tasks often require many labeled training examples that are expensive to obtain. Recent advancements in machine teaching, specifically the data programming paradigm, facilitate the creation of training data sets quickly via a general framework for building weak models, also known as labeling functions, and denoising them through ensemble learning techniques. We present a fast, simple data programming method for augmenting text data sets by generating neighborhood-based weak models with minimal supervision. Furthermore, our method employs an iterative procedure to identify sparsely distributed examples from large volumes of unlabeled data. The iterative data programming techniques improve newer weak models as more labeled data is confirmed with human-in-loop. We show empirical results on sentence classification tasks, including those from a task of improving intent recognition in conversational agents. Comment: 6 pages, 2 figures, In Proceedings of the AAAI Conference on Artificial Intelligence 2020 (IAAI Technical Track: Emerging Papers) |
Databáze: | OpenAIRE |
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