Automated Metaphor Identification in Russian and Its Implications for Metaphor Studies
Ch. 8. P. 86-96.
, , in: Advances in Cognitive Research, Artificial Intelligence, and Neuroinformatics. .: Springer, 2020..
The paper gives an account of a system for automated identification of linguistic metaphor in Russian text. The design of the system is based on the five features: semantic heterogeneity, lexical and morphosyntactic metaphor association, concreteness-abstractness, and topic vectors. Since each of these features is motivated by a specific set of assumptions about the linguistic and the cognitive nature of ...
Added: October 7, 2020
Reducing False Positives in Bank Anti-fraud Systems Based on Rule Induction in Distributed Tree-based Models
, , Computers and Security 2022 Vol. 120
Fraud detection in bank payments transactions suffers from a high number of false positives. To deal with this problem, we introduce a rules generation framework for a fraud-detection system – an automatic rules generation using distributed tree-based ML (machine learning) algorithms such as Decision Tree, Random Forest and Gradient Boosting, where the components of expert ...
Added: June 8, 2022
, , et al., , in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019). Issue 18.: M.: Russian State University for the Humanitie, 2019.. P. 163-176.
The paper considers the task of automatic discourse parsing of texts in Russian. Discourse parsing is a well-known approach to capturing text semantics across boundaries of single sentences. Discourse annotation was found to be useful for various tasks including summarization, sentiment analysis, question-answering. Recently, the release of manually annotated Ru-RSTreebank corpus unlocked the possibility of ...
Added: October 16, 2019
, , , in: Artificial Intelligence and Natural Language, 7th International Conference, AINL 2018, St. Petersburg, Russia, October 17–19, 2018, Proceedings. Issue 930.: Switzerland: Springer, 2018.. Ch. 3. P. 23-34.
The paper presents a supervised machine learning experiment with multiple features for identification of sentences containing verbal metaphors in raw Russian text. We introduce the custom-created training dataset, describe the feature engineering techniques, and discuss the results. The following set of features is applied: distributional semantic features, lexical and morphosyntactic co-occurrence frequencies, flag words, quotation ...
Added: August 30, 2018