Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training

By Giannis Karamanolakis, Daniel J. Hsu, Luis Gravano. 2019

40 citations recorded by the source corpus.

User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be useful for both users and downstream opinion mining applications. Current supervised approaches for learning aspect classifiers require many fine-grained aspect labels, which are labor-intensive to obtain. And, unfortunately, unsupervised topic models often fail to capture the aspects of interest. In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i.e., weakly positive indicators) for the aspects of interest. First, we show that current weakly supervised approaches fail to leverage the predictive power of seed words for aspect detection. Next, we propose a student-teacher approach that effectively leverages seed words in a bag-of-words classifier (teacher); in turn, we use the teacher to train a second model (student) that is potentially more powerful (e.g., a neural network that uses pre-trained word embeddings). Finally, we show that iterative co-training can be used to cope with noisy seed words, leading to both improved teacher and student models. Our proposed approach consistently outperforms previous weakly supervised approaches (by 14.1 absolute F1 points on average) in six different domains of product reviews and six multilingual datasets of restaurant reviews.

Subfields: Artificial Intelligence

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Citations in the corpus

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2020, unknown): “For aspect classification, Karamanolakis et al. (2019) create a simple bag-of-words classifier on a list of seed words and train a deep neural network on its weak supervision.”
Co-training Improves Prompt-based Learning for Large Language Models (2022, methodology): “Karamanolakis et al. (2019, 2021) employ self-training and iterative co-training with weak supervision as the initial label signal, and they similarly use a neural network with pretrained embeddings as a downstream model.”