Abstract
Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in modern natural language processing concerning the automatic extraction of (aspect phrase, opinion phrase, sentiment polarity) triplets from a given text. Current state-of-the-art methods achieve relatively high results by analyzing all possible spans extracted from a text. Due to a high number of analyzed spans, span-level methods usually apply some kind of pruning operators that interrupt the gradient flow. They also do not analyze interrelations between spans while constructing model output, relying on independent, sequential predictions for candidate triplets. This paper presents a new span-level approach that applies a learnable extractor of spans and a differentiable span selector that enables end2end training. The approach relies on a fully connected pairwise CRF model to capture interrelations between spans while constructing the output. Conducted experiments demonstrated that the proposed approach achieves superior results in terms of F1-score in comparison to other, state-of-the-art ASTE methods.
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Notes
- 1.
Our approach constructs triplets exploiting interrelations between phrases in the last layer of neural network. Splitting phrases into separate sets of aspect and opinion phrases at an earlier stage of processing would hinder the benefits of assigning phrase types while considering the entire triplet.
- 2.
The pseudocode of the decoding procedure is available in the online appendix: https://www.cs.put.poznan.pl/mlango/publications/pakdd23.pdf.
- 3.
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Acknowledgement
This research has received funding from the National Center for Research and Development under the Infostrateg program (project: INFOSTRATEG-III/0003/2021-00 “Development of an IT system using AI to identify consumer opinions on product safety and quality” realized in a consortium of Poznan Institute of Technology and Poznan University of Technology).
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Naglik, I., Lango, M. (2023). Exploiting Phrase Interrelations in Span-level Neural Approaches for Aspect Sentiment Triplet Extraction. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13938. Springer, Cham. https://doi.org/10.1007/978-3-031-33383-5_18
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