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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 34

Iterative Data Programming for Expanding Text Classification Corpora

February 1, 2023

Authors

Neil Mallinar

Pryon Research


Abhishek Shah

IBM Watson


Tin Kam Ho

IBM Watson


Rajendra Ugrani

IBM Watson


Ayush Gupta

IBM Watson


Published:

2020-06-02

Proceedings:

Proceedings of the AAAI Conference on Artificial Intelligence, 34

Volume

Issue:

Vol. 34 No. 08: AAAI-20 / IAAI-20 Technical Tracks

Track:

IAAI Technical Track: Emerging Papers

Downloads:

Download PDF

Abstract:

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.

DOI:

10.1609/aaai.v34i08.7045


AAAI

Vol. 34 No. 08: AAAI-20 / IAAI-20 Technical Tracks


ISSN 2374-3468 (Online) ISSN 2159-5399 (Print) ISBN 978-1-57735-835-0 (10 issue set)


Published by AAAI Press, Palo Alto, California USA Copyright © 2020, Association for the Advancement of Artificial Intelligence All Rights Reserved

Topics: AAAI

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