Deep Weighted MaxSAT for Aspect-based Opinion Extraction
| Autor: | Wenya Wang, Sinno Jialin Pan, Meixi Wu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Deep learning Inference 010501 environmental sciences Semantics 01 natural sciences 030507 speech-language pathology & audiology 03 medical and health sciences Maximum satisfiability problem Domain knowledge Artificial intelligence Graphical model 0305 other medical science Structured prediction business Rule of inference Boolean satisfiability problem Feature learning 0105 earth and related environmental sciences |
| Zdroj: | EMNLP (1) |
| DOI: | 10.18653/v1/2020.emnlp-main.453 |
| Popis: | Though deep learning has achieved significant success in various NLP tasks, most deep learning models lack the capability of encoding explicit domain knowledge to model complex causal relationships among different types of variables. On the other hand, logic rules offer a compact expression to represent the causal relationships to guide the training process. Logic programs can be cast as a satisfiability problem which aims to find truth assignments to logic variables by maximizing the number of satisfiable clauses (MaxSAT). We adopt the MaxSAT semantics to model logic inference process and smoothly incorporate a weighted version of MaxSAT that connects deep neural networks and a graphical model in a joint framework. The joint model feeds deep learning outputs to a weighted MaxSAT layer to rectify the erroneous predictions and can be trained via end-to-end gradient descent. Our proposed model associates the benefits of high-level feature learning, knowledge reasoning, and structured learning with observable performance gain for the task of aspect-based opinion extraction. |
| Databáze: | OpenAIRE |
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