# LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification
## 4 Experiments
### Ablations and Variations of LCF Design
#### 4.5.2 Ablate CDM/CDW Layer  
The CDM layer and CDW layer are core architecture for the LCF design. We ablate the CDM and CDW layer and utilize the global context features to predict sentiment polarities for targeted aspects.  
For the LCF-GloVe ablation experiment, LCF-GloVe with \"only Glo\" means that only global context features are captured and learned, and no CDM or CDW layer is deployed in this ablation. This ablation achieves inferior performance among three datasets, which indicates that CDM layer and CDW layer are very effective for LCF design models. LCF-GloVe with \"only CDM\" performs moderate performance among three datasets, and its performance on the restaurant dataset is equal to the baseline model. Moreover, LCF-GloVe with \"only CDW\" attains worse performance on three datasets compared to LCF-GloVe with \"only Glo\".  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Ablations**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Restaurant**} & \multicolumn{2}{c}{**Twitter**} \\ \cline{3-8}  & & **Accuracy (\%)** & **F1 (\%)** & **Accuracy (\%)** & **F1(\%)** & **Accuracy (\%)** & **F1 (\%)** \\ \hline \multirow{2}{*}{LCF-GloVe} & Glo-CDM & **76.02** & **70.58** & **82.5** & **73.92** & **72.25** & **70.92** \\  & Glo-CDW & **75.24** & **71.46** & **81.61** & **72.26** & **71.82** & **69.83** \\ \hline \multirow{6}{*}{LCF-GloVe} & Glo-CDM w/o PFE & 73.04 & 68.17 & 82.32 & 74.32 & 72.83 & 71.09 \\  & Glo-CDW w/o PFE & 74.14 & 69.84 & 81.07 & 72.05 & 71.97 & 70.2 \\  & Only Glo & 74.29 & 70.52 & 80.8 & 71.38 & 71.24 & 69.13 \\  & Only CDM & 74.76 & 69.68 & 82.32 & 74.4 & 71.68 & 69.91 \\  & Only CDW & 74.92 & 70.65 & 80.36 & 70.54 & 71.39 & 67.99 \\  & Glo-CDM w/o FIL & 74.14 & 69.25 & 82.23 & 74.02 & 72.25 & 69.57 \\  & Glo-CDW w/o FIL & 74.61 & 69.33 & 80.98 & 71.49 & 71.1 & 69.85 \\ \hline \multirow{2}{*}{LCF-BERT} & Glo-CDM & **82.29** & **79.28** & **86.52** & **80.4** & **76.45** & **75.52** \\  & Glo-CDW & **82.45** & **79.59** & **87.14** & **81.74** & **77.31** & **75.78** \\ \hline \multirow{6}{*}{LCF-BERT} & Only Glo & 80.88 & 77.52 & 86.25 & 79.56 & 76.16 & 75.01 \\  & Only CDM & 80.72 & 76.65 & 83.66 & 75.2 & 75.14 & 74.54 \\ \cline{1-1}  & Only CDW & 81.03 & 77.22 & 84.64 & 77.08 & 75.29 & 73.78 \\ \cline{1-1}  & Glo-CDM w/o FIL & 81.03 & 77.5 & 85.98 & 79.53 & 75.14 & 75.16 \\ \cline{1-1}  & Glo-CDW w/o FIL & 80.56 & 76.23 & 85.98 & 80.08 & 76.3 & 75.16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of LCF-GloVe variations of SRD threshold. “Only” means only global context or local context participates in the learning process. “w/o” means “without”. The best scores of LCF-GloVe and LCF-BERT are at the top of the table. SRD thresholds for each variation are equal to basic CDM/CDW design (see Table 5).  
For the LCF-BERT ablation experiment, LCF-BERT with \"only Glo\" attains a considerable performance compared to LCF-BERT with \"only CDM\" and LCF-BERT with \"only CDW\". However, there is still a gap of performance on three datasets between both three ablations and their baseline model.