## 3 Results
### Methods to Evaluate the Similarity of Vocabulary  
Here we will briefly present the NLP methods used in the study. The efforts have been concentrated on evaluating the possibilities of recognizing the financial operation using the accountant's comments. First, we evaluated the vocabulary of comments used in different operations. To evaluate the similarity between the vocabulars two methods were used: bag of words and cosine similarity were applied to get the general impression about proximity of different vocabularies. Cosine similarity is a simple and efficient measure to show the similarity between different vocabularies and hence the potential to recognize the financial operation using the comments. Then tf-idf and GloVe word embedding were used to find the accuracy of the recognition of financial operations using the accountant comments only. This could serve as a fast way to identify the possible mistakes made by accountants or other types of irregularities. Additionally, we checked how the comments next to the sales and purchases operations could be used to identify possible irregular sales or purchases (typically, such operations form the largest part of all financial operations in many business companies). The main hypothesis was that semantically similar comments need to be associated with similar amounts of money. One of the best initial ways to obtain a general understanding of how different sets of sentences or other pieces of texts differs is to count the term frequencies in those texts (bag-of-words approach). The method is fast and can provide good primary insights into the potential to use NLP methods for operation recognition using comments. At the same time, it needs to be noted that the method also is very simple and flexible. It could be used in many ways and many applications for feature extraction from textual documents. A bag-of-words is a characterization of text using the occurrences of words in a particular document or groups of documents [20,21]. It involves two things:  
1. A vocabulary of known words. 2. Known words presence measure. The name bag of words is used because information about the order of words in the document or corpora is discarded as well as the information about the structure of the document or part of the document. The model takes into account only if known words occur in the document or not but not in which place of the document. In the case of accountant comments, this fact is not so important because comments are typically short expressions, usually no longer than five lexical units (words or abbreviations). Intuition tells us that similar documents (having similar bags of words) need to have similar content too. Moving further, we can expect that from the content alone, we can obtain more or less information about the meaning of the document. At the same time, this method could be treated as simple and complex. The complexity arises from two factors: how to design the vocabulary of known words (or tokens) and rank known words [22]. Another NLP technique used in this study was cosine similarity. Here we used cosine similarity to measure the similarity between comments associated with different types of financial operations. Mathematically cosine similarity measures the proximity between two vectors in an inner product space. This similarity is expressed by calculating the cosine of the angle between two vectors. The cosine tells if two vectors are pointing in approximately the same direction. It is a well-known and widely used technique that is popular in trying to evaluate the similarity between documents. In this way, a document can be described using very many parameters, each of them describing the frequency of a particular word or phrase in the analyzed document. Each document is represented by the term-frequency vector. In mathematical terms, this could be expressed as follows [23].