Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis
Authors: Bowen Xing, Lejian Liao, Dandan Song, Jingang Wang, Fuzheng Zhang, Zhongyuan Wang, Heyan Huang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Sem Eval-2014 Datasets demonstrate the effectiveness of AA-LSTM. |
| Researcher Affiliation | Collaboration | Bowen Xing1 , Lejian Liao1 , Dandan Song1 , Jingang Wang 2 , Fuzhen Zhang2 , Zhongyuan Wang2 and Heyan Huang1 1Lab of High Volume language Information Processing & Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science & Technology, Beijing Institute of Technology 2Meituan-Dianping Group |
| Pseudocode | No | The paper provides mathematical equations (1-9) for the AA-LSTM network but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access (link or explicit statement of release) to open-source code for the methodology described. |
| Open Datasets | Yes | We experiment on Sem Eval 2014 [Pontiki et al., 2014] task 4 datasets which consist of laptop and restaurant reviews and are widely used benchmarks in many previous works |
| Dataset Splits | Yes | 20% of the training data is used as the development set. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like TensorFlow, Glove, and Adam optimizer, but does not provide specific version numbers for these dependencies, which are necessary for reproducible descriptions. |
| Experiment Setup | Yes | All embedding dimensions are set to 300 and the batch size is set as 16. We minimize the loss function to train our models using Adam optimizer [Diederik and Jimmy, 2014] with the learning rate set as 0.001. To avoid over fitting, we adopt the dropout strategy with p = 0.5 and the coefficient of L2 normalization in the loss function is set to 0.01. |