Proceedings of the Computational Models in Language and Speech Workshop (CMLS 2020)
Morphemic structure of words is useful for various NLP problems, in particular, for deriving a meaning of unknown words in languages with rich morphology, such as Russian. For Russian, several neural network models for automatic morpheme segmentation of words were built, but only for parsing their lemmas. Meanwhile, significantly varying word forms are present in texts, among them unknown words are often encountered, and their lemmas are unknown. The paper reports on experiments for comparing two ways to automatically segment Russian word forms, both ways involve splitting into morphs and classification of resulted morphs. The former is based on a neural model trained on a data set automatically augmented with segmented word forms, the latter produces segmentation through predicted lemma and a pre-trained neural morpheme segmentation model for lemmas. It was shown that the models have comparable quality in morpheme segmentation and classification, and the model based on the augmented dataset slightly outperforms in word-level classification accuracy.
Network analysis as a method has applications in a wide range of fields from physics to epidemiology and from sociology to political science, and in the meantime has also reached the literary studies. Networks can be leveraged to examine intertextual relations or even artistic influences, but the main application so far has been the analysis of social formations and character interactions within fictional worlds. To make this possible, texts have to be formalized into a set of nodes and edges, where nodes represent characters and edges describe the relations between these characters in a very simple fashion: Do they or don’t they interact? Based on a selection of Russian plays and Tolstoy’s novel War and Peace, we will describe approaches to the social network analysis of literary texts.
Proceedings of the 26th Conference of Open Innovations Association FRUCT, Helsinki, Finland.
Currently, social network sites tend to be one of the major communication platforms in both offline and online space. Freedom of expression of various points of view, including toxic, aggressive, and abusive comments, might have a long-term negative impact on people’s opinions and social cohesion. As a consequence, the ability to automatically identify and moderate toxic content on the Internet to eliminate the negative consequences is one of the necessary tasks for modern society. This paper aims at the automatic detection of toxic comments in the Russian language. As a source of data, we utilized anonymously published Kaggle dataset and additionally validated its annotation quality. To build a classification model, we performed fine-tuning of two versions of Multilingual Universal Sentence Encoder, Bidirectional Encoder Representations from Transformers, and ruBERT. Finetuned RuBERT achieved F1 = 92.20%, demonstrating the best classification score. We made trained models and code samples publicly available to the research community.
he central part of the project involves the development of the comprehensive representative Russian Corpus of Academic Texts (CAT). Following well-established corpus development procedures (e.g., BAWE). Texts in the CAT corpus are sourced from six general disciplinary fields: social studies, political science and international relations, law, linguistics, economics, psychology and education science. The discipline sub-corpora consist of about 370 to 480 thousands tokens, amounting to approximately 2 mln. tokens in the corpus in general. Texts entered in CAT are supplied with metalinguistic, morphological and syntactic annotations, carried out with the help of the Universal Dependencies pipeline (Straka et al. 2017). CAT is outfitted with built-in data processing tools, which allows for evaluation of texts written by novice writers of Academic Russian.
This book constitutes the proceedings of the 8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019.
The 24 full papers and 10 short papers were carefully reviewed and selected from 134 submissions (of which 21 papers were rejected without being reviewed). The papers are organized in topical sections on general topics of data analysis; natural language processing; social network analysis; analysis of images and video; optimization problems on graphs and network structures; analysis of dynamic behaviour through event data.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.