## Appendix B Features Based on MESHRUS Concepts  
MeSH Russian (MESHRUS) [68] is a Russian version of the Medical Subject Headings (MeSH) database (home page of the MeSH database website: [[https://www.nlm.nih.gov/mesh/meshhome.html](https://www.nlm.nih.gov/mesh/meshhome.html)]([https://www.nlm.nih.gov/mesh/meshhome.html](https://www.nlm.nih.gov/mesh/meshhome.html)), accessed on 12 December 2021). MeSH is a dictionary designed for indexing biomedical information that contains concepts from scientific journal articles and books, and is intended for their indexing and searching. The MeSH database is filled from articles in English; however, there exist translations of the database to different languages. We used the Russian version, MESHRUS. It is a less complete analogue of the English version: for example, it does not contain concept definitions. MESHRUS contains a set of tuples \((k;v)\) matching Russian concepts \(k\) with their relevant CUI codes \(v\) from the UMLS theaurus. A concept \(k\) can consist of a word or a sequence of words. The following pre-processing algorithm is used: words are lemmatized, put into a single register and filtered by length, frequency and parts of speech. In order to automatically find concepts from MESHRUS corresponding to words from our corpus, we perform two approaches. The first approach is to map the filtered words \(W=\{w_{i}\}_{i=0}^{N}\) from the corpus to MESHRUS concepts \(\{C_{j}\}\). As a criterion for comparing words and concepts, we use the cosine similarity between their vector representations obtained using the FastText [45] model (see Section 4.2.4): a word \(w_{i}\) is assigned the CUI code \(v_{j}\) (see Figure 11) whose corresponding concept \(C_{j}\) has the highest similarity measure \(\cos\bigl{(}\operatorname{FastText}(w_{i}),\operatorname{FastText}(C_{j}) \bigr{)}\). If this similarity measure is lower than the empirical threshold \(T=0.55\), no CUI code is assigned to \(w_{i}\). Here, \(\operatorname{FastText}(C_{j})\) is the vector representation of the output of concept \(C_{j}\) obtained by processing words of \(C_{j}\) to FastText, encoded as a sequence of n-grams. The second approach is based on the mapping of syntactically and lexically related phrases extracted on the sentence level. Prepositions, particles and punctuation are not taken into account. Syntactic relations are obtained from dependency trees generated with UDpipe v2.5. For each word \(w_{i}\subset W\), its adjacent words \([w_{i-1},w_{i+1}]\) are selected. Together with \(w_{i}\) itself, they form a lexical set \(w_{i_{j}}\). Then, for the current word \(w_{i}\), we find the word \(w_{i_{\text{parent}}}\) that is its parent in the dependency tree (if there is no parent, then the syntactic set contains only \(w_{i}\)). These \(w_{i_{j}}\) and \(w_{i_{\text{parent}}}\) in turn form a syntactic set \(w_{i_{j}}\). Similarly, such lexically and syntactically related sets \(c_{j_{l}}\) and \(c_{j_{k}}\) are formed for each filtered word \(c_{j}\) of the concept \(C_{k}\) from the MESHRUS dictionary: \(c_{j_{l}}=[c_{j-1},c_{j},c_{j+1}]\) and \(c_{j_{k}}=[c_{j},c_{j_{\text{parent}}}]\). Then, for each word \(w_{i}\subset W\) and word \(c_{j}\subset C_{k}\), by analogy with the literature [69], the following metrics are calculated:  
1. \(\text{lexical\_involvement}(w_{i},c_{j})=F_{1}\bigg{(}\frac{|w_{i_{l}}\cap c_{j _{l}}|}{|w_{i_{l}}|},\frac{|w_{i}\cap c_{j_{l}}|}{|c_{j_{l}}|}\bigg{)}\);
2. \(\text{cohesiveness}(w_{i},c_{j})=F_{1}\big{(}\frac{|w_{i_{l}}\cap c_{j_{l}}|}{| w_{i_{l}}|},\frac{|w_{i_{l}}\cap c_{j_{l}}|}{|c_{j_{l}}|}\big{)}\);
3. \(\text{centrality}\), which is \(1\) if the word \(w_{i_{\text{parent}}}\) of the syntax set \(w_{i_{k}}\) is represented in the syntax set \(c_{j_{k}}\) of words from the dictionary; \(0\) otherwise. Here, \(F_{1}(x,y)\) is the harmonic mean of \(x\) and \(y\), \(|N|\) denotes the length of set \(N\) and \(M\cap N\) is the intersection of the two sets.