## 5 Discussion  
The research discussed in this paper focuses on comparing machine learning classifiers with deep learning models that consist of an empirical assessment. In our paper, the theory, arguments, and solution methods proposed were empirically tested. Even though the dataset size was small, the high accuracy of the models indicates that our models were highly efficient. We addressed our views on how to improve the accuracy of classification using different methods. We performed hyperparameter tuning to improve the accuracy for the used models. We provided an overview of some of the potential ways to increase accuracy. We did our best to minimize the challenges to the problems of validity. We extracted various characteristics from the sentence, but we did not conduct any relation or meta-data analysis that could be regarded as external variables.  
We used a dataset that is available in the public domain. The dataset was annotated and checked by more than one person [48], ensuring that the annotation of the dataset was of good quality and that no errors in the annotation and calculation were present. Moreover, we carried out the experiments more than once to ensure that there were no mistakes when performing the experiments and that our findings were replicable.  
We would also like to highlight the challenges we faced while performing the classifications. The first hurdle was locating the correct dataset for our study. Then, it was challenging to apply various ML and neural networks to the feature list present in the dataset. The next issue we faced was identifying various permutations and combinations of the models and the data training approach discussed in the paper. To ensure that our algorithms were robust, we tried various approaches and models, but mentioned only the significant ones in this paper. This was the most challenging part of our analysis.  
Next, we would talk about the future scope of our experiments. In future research, we would like to have a few valuable additions. Firstly, our corpus consisted entirely of scientific articles from computer linguistic research, constituting a particular subset of textual information. We want to test our model on scientific articles from different countries and domains of science. We used an existing dataset; hence, we could not change the existing parameters, such as the feature set, the synonyms of problem words, the synonyms of solution words and the number of sentences in the corpus. In the future, we would like to prepare our corpus. It would offer us more flexibility in the way we approach our research question. We would like to make a corpus that contains articles from different domains. It would help us to identify different characteristics of problem-solution patterns specific to a research domain. We would like to apply the model to a corpus containing research articles from multiple research domains and to try to find a generalized model for problem-solution classification. Once we achieve a generalized model for problem-solution classification, the next stage of our research would be linking the problem string with its corresponding solution string. This would help in knowledge mining from research articles which would eventually lead to idea discovery from the scientific texts. This would improve the existing mechanisms of text summarization and text abstraction. It would also help review articles and help in the classification of research domains based on the texts from the research papers.