Summary of Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency, by Yuchen Shi et al.
Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency
by Yuchen Shi, Deqing Yang, Jingping Liu, Yanghua Xiao, Zongyu Wang, Huimin Xu
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Syntax&Semantic-Enhanced Negation Extraction (SSENE) model achieves state-of-the-art performance on the newly introduced Negation Triplet Extraction (NTE) task. NTE aims to identify negation subjects, cues, and scopes in natural language text. SSENE utilizes a generative pretrained language model with an Encoder-Decoder architecture and incorporates syntactic dependency trees to capture correlations between these entities. Additionally, the model is trained with a multi-task learning framework that includes an auxiliary task ensuring semantic consistency between the input sentence and extracted triplets. Evaluations on the NegComment dataset, constructed from real-world user reviews, demonstrate SSENE’s superiority over baselines. Ablation studies show that incorporating syntactic information improves distant dependency recognition, while the auxiliary task enhances semantic consistency in extracted negation triplets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand negations in sentences by identifying the subject, cue, and scope of negation. This is important because it can help computers better understand what people are saying about things they don’t like. The researchers created a new model called SSENE that uses artificial intelligence and special techniques to do this task well. They tested their model on a large dataset of real-world reviews from the Meituan platform and found that it performed better than other models. This is important because it can help computers understand people’s opinions and feelings about things. |
Keywords
» Artificial intelligence » Encoder decoder » Language model » Multi task » Syntax
