# Global and Local Information Adjustment for Semantic Similarity Evaluation
## 6 Conclusions  
In this study, the semantic similarity of sentence pairs is evaluated using global and local sentence information. The proposed model consists of Bi-LSTM, self-attention, and capsule network, and all neural networks, except self-attention, apply the Siamese network. This model extracts global features through Bi-LSTM and extracts local features through the capsule network. The extracted global and local features are used when evaluating the semantic similarity of sentence pairs using Manhattan distance. This allows not only global and local features to be considered together but also the adjustment of which information to focus more closely on by \(\alpha\). As a result of comparing the existing models, models using only global features, models using only local features, and the model using both global and local features at the same time, it can be seen that the accuracy of the model using both global and local features at the same time is higher.  
However, this study has a limitation. Even though \(\alpha\) plays a very important role in determining the weight of global and local features, we have not been able to come up with a universal methodology for obtaining \(\alpha\). We will find a way to integrate this into the entire network to obtain the optimal \(\alpha\) through learning. We will also apply another language model instead of Word2 Vec to obtain more sophisticated sentence pair representations, and update the current network structure after analyzing the strengths and weaknesses to further improve the accuracy of the model.  
**Author Contributions:** Conceptualization, T.-S.H. and Y.-S.K.; Data curation, T.-S.H.; Formal analysis, T.-S.H., J.-D.K. and C.-Y.P.; Methodology, T.-S.H.; Project administration, Y.-S.K.; Resources, T.-S.H.; Software, T.-S.H.; Supervision, Y.-S.K.; Validation, J.-D.K. and C.-Y.P.; Writing--original draft, T.-S.H.; Writing--review and editing, Y.-S.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C2006010).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://code.google.com/archive/p/word2](https://code.google.com/archive/p/word2) vec/]([https://code.google.com/archive/p/word2](https://code.google.com/archive/p/word2) vec/); [[http://nlp.kookmin.ac.kr](http://nlp.kookmin.ac.kr)]([http://nlp.kookmin.ac.kr](http://nlp.kookmin.ac.kr)); [[https://www.kaggle.com/quora/question-pairs-dataset](https://www.kaggle.com/quora/question-pairs-dataset)]([https://www.kaggle.com/quora/question-pairs-dataset](https://www.kaggle.com/quora/question-pairs-dataset)); [[http://kin.naver.com/](http://kin.naver.com/)]([http://kin.naver.com/](http://kin.naver.com/)); [[http://aiopen.etri.re.kr/service_dataset.php](http://aiopen.etri.re.kr/service_dataset.php)]([http://aiopen.etri.re.kr/service_dataset.php](http://aiopen.etri.re.kr/service_dataset.php)).  
**Conflicts of Interest:** The authors declare no conflict of interest.