We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for jointly handling the extraction tasks of aspects and opinions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework.
Li and Lam REF adopted two LSTMs for jointly handling the extraction tasks of aspects and opinions via memory interactions.
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction
EMNLP
Computer Science