## 3 Results
### Dataset Used and the Results of the Operation Type Recognition Using Comments
In all other cases, the similarity between the vocabularies used in comments next to different types of financial operations is negligible. This indicates that the content of comments has a high potential to help recognize financial operations. At the next stage, we performed operation recognition experiments using accountant comments. A threefold cross-validation check has been used in this experiment. We took 20 percent of randomly selected records from each of the eight types of financial operations for testing, while the remaining 80 percent of records were left for training. Abbreviations found in comments were transformed into words using the Dutch abbreviations lexicon (758 abbreviations). Abbreviations that were not present in the lexicon (32 abbreviations) were treated as an appropriate word. The recognition was performed as follows: the tf-idf value has been calculated for every word for all eight types of financial operations. If the comment had several words, the total tf-idf value of the comment was equal to the sum of tf-idf values of each word in the comment. The comment was assigned to the class of financial operations with the highest total tf-idf value. Table 2 summarizes the accuracy of recognition of financial operations using accountant comments. The results show that comments allow for recognizing the class of performed financial operations quite accurately. This suggests that they could be used as a supportive tool when looking for potential errors made by accountants. Even better recognition results were achieved by applying the same method for the comments within the same company. Two randomly selected companies were chosen to check the operation recognition accuracy using the comments from the same company only. An 80-20 percent cross-validation has been applied. In this case, the average operation accuracy was 99.5 percent, while the worst accuracy was 98.9 percent. Finally, we used word embedding methods to evaluate the discrimination ability of accountant comments. In this case, we applied the Global Vectors or GloVe algorithm as one of the best-suited \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline
2 & 0.0 & 1 & & & & & & \\
3 & 0.292 & 0.001 & 1 & & & & & \\
4 & 0.058 & 0.023 & 0.038 & 1 & & & & \\
5 & 0.018 & 0.019 & 0.057 & 0.034 & 1 & & & \\
6 & 0.041 & 0.028 & 0.059 & 0.308 & 0.045 & 1 & & \\
7 & 0.002 & 0.008 & 0 & 0.122 & 0.007 & 0.278 & 1 & \\
8 & 0.007 & 0.003 & 0.104 & 0.071 & 0.155 & 0.158 & 0.178 & 1 \\ \hline \end{tabular}
\end{table} 
Table 1: The cosine similarity between vocabularies used in comments next to different types of financial operations. In this table, 1 means shareholder operations, 2—operations with salaries, 3—return operations, 4—other operations, 5—other financial operations, 6—residual operations, 7—depreciation operations, 8—sales operations. word embedding methods for the task [26]. The methods of experiments were very similar as in the case with the tf-idf experiments. It means that a threefold cross-validation check has been used in this experiment. Each time we took 20 percent of randomly selected records from each of the eight types of financial operations for testing, while the remaining 80 percent of records were left for training. Abbreviations found in comments were transformed into words. Then for the comments associated with each of the eight types of financial operations, GloVe training has been performed. During recognition, a comment has been assigned to the class with the highest word vector value. The results are presented in Table 3.  
The results obtained with GloVe need to be treated as very accurate. Still hard to say if these observations could be confirmed using data from other countries too. Table 4 summarizes the average recognition accuracies of different financial operations using accountant comments and tf-idf and GloVe methods. The GloVe accuracy outperformed the results obtained using tf-idf.