Negative Focus Detection via Hypergraph Attention LSTM Network and LLM-Driven Data Augmentation
Zhong QIAN , Peifeng LI , Qiaoming ZHU , Guodong ZHOU
Negative focuses are the most prominent negated texts for a negative cue or verbal negation in a negative statement. Although previous work adopted sequence labelling framework using LSTMand CRF networks, Negative Focus Detection (NFD) is still faced with several disadvantages involving with data limitation, coarse-grained encoding, and insufficient dependencies of the sequence of words. To solve these problems, we firstly apply data augmentation driven by Large Language Models (LLMs) to produce more samples. Then, we propose a novel HyperGraph attention LSTM network (LSTM-HyG) to capture high-level semantics for sentences, negated verbs, negative cues. Finally, we predict negative focuses by a fine-grained label scheme that can learn adequate sequential dependency relationship of words. Experimental results on PB-FOC and CNeSp datasets can prove that our proposed model is superior to state-of-the-arts.
Negative Focus / LLM-DrivenData Augmentation / HyperGraph Attention Network
Higher Education Press
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