Exploiting Phrase Interrelations in Span-level Neural Approaches for Aspect Sentiment Triplet Extraction
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee
2023
chapter in monograph / paper
english
- aspect sentiment triplet extraction
- span-level approaches
- sentiment analysis
- natural language processing
- deep learning
EN 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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