The paper reports the results of a translationese study of literary texts based on translated and non-translated Russian. We aim to find out if translations deviate from non-translated literary texts, and if the established differences can be attribu
ted to typological relations between source and target languages. We expect that literary translations from typologically distant languages should exhibit more translationese, and the fingerprints of individual source languages (and their families) are traceable in translations. We explore linguistic properties that distinguish non-translated Russian literature from translations into Russian. Our results show that non-translated fiction is different from translations to the degree that these two language varieties can be automatically classified. As expected, language typology is reflected in translations of literary texts. We identified features that point to linguistic specificity of Russian non-translated literature and to shining-through effects. Some of translationese features cut across all language pairs, while others are characteristic of literary translations from languages belonging to specific language families.
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated named entities and 39K annotated relations. Its i
mportant difference from previous datasets is annotation of nested named entities, as well as relations within nested entities and at the discourse level. NEREL can facilitate development of novel models that can extract relations between nested named entities, as well as relations on both sentence and document levels. NEREL also contains the annotation of events involving named entities and their roles in the events. The NEREL collection is available via https://github.com/nerel-ds/NEREL.
Feature engineering is an important step in classical NLP pipelines, but machine learning engineers may not be aware of the signals to look for when processing foreign language text. The Russian Feature Extraction Toolkit (RFET) is a collection of fe
ature extraction libraries bundled for ease of use by engineers who do not speak Russian. RFET's current feature set includes features applicable to social media genres of text and to computational social science tasks. We demonstrate the effectiveness of the tool by using it in a personality trait identification task. We compare the performance of Support Vector Machines (SVMs) trained with and without the features provided by RFET; we also compare it to a SVM with neural embedding features generated by Sentence-BERT.
We develop a minimally-supervised model for spelling correction and evaluate its performance on three datasets annotated for spelling errors in Russian. The first corpus is a dataset of Russian social media data that was recently used in a shared tas
k on Russian spelling correction. The other two corpora contain texts produced by learners of Russian as a foreign language. Evaluating on three diverse datasets allows for a cross-corpus comparison. We compare the performance of the minimally-supervised model to two baseline models that do not use context for candidate re-ranking, as well as to a character-level statistical machine translation system with context-based re-ranking. We show that the minimally-supervised model outperforms all of the other models. We also present an analysis of the spelling errors and discuss the difficulty of the task compared to the spelling correction problem in English.
We present a manually annotated lexical semantic change dataset for Russian: RuShiftEval. Its novelty is ensured by a single set of target words annotated for their diachronic semantic shifts across three time periods, while the previous work either
used only two time periods, or different sets of target words. The paper describes the composition and annotation procedure for the dataset. In addition, it is shown how the ternary nature of RuShiftEval allows to trace specific diachronic trajectories: changed at a particular time period and stable afterwards' or was changing throughout all time periods'. Based on the analysis of the submissions to the recent shared task on semantic change detection for Russian, we argue that correctly identifying such trajectories can be an interesting sub-task itself.
This paper presents the implementation of a bilingual term alignment approach developed by Repar et al. (2019) to a dataset of unaligned Estonian and Russian keywords which were manually assigned by journalists to describe the article topic. We start
ed by separating the dataset into Estonian and Russian tags based on whether they are written in the Latin or Cyrillic script. Then we selected the available language-specific resources necessary for the alignment system to work. Despite the domains of the language-specific resources (subtitles and environment) not matching the domain of the dataset (news articles), we were able to achieve respectable results with manual evaluation indicating that almost 3/4 of the aligned keyword pairs are at least partial matches.
The Russian and German journeys are considered one of the most
important European journeys which described some of the Holy Places
(Palestine) and its economy, especially agriculture during the Crusades. This
research will talk about models of Russian and German flights, and the
definition of their owners.
Russian-Iranian relations began crystallize and expand significantly after the
end of the Cold War and the disintegration of the Soviet Union in 1991, but
these relations seemed cautious in the beginning, despite the existence of
common interests
between both countries represented to strengthen Iran's
military and economic capabilities, Russia considered as a supportive partner
in this field, in addition to nuclear cooperation where Russia was the only
country that has not been subject to pressure from the United States and
accepted the signing of the contract for the establishment of the Iranian
nuclear reactor, In contrast, Russia has considered these relationships an
opportunity to improve its economy, which suffered a strong tremor after the
disintegration of the Soviet Union.
International relations have witnessed great changes after the
collapse of the Soviet Union, and the emergence of the United
States pole sole controlled the fate of international relations, and
the impact of these changes significant to the Russia
n Federation
and the heir of the Soviet Union, as experienced international
pressure continuing to encircle and isolate and control their own
destiny, but it did not stand idle in front of this pressures, and was
able, thanks to a set of political and economic variables and
procedures for the Advancement again and restore its role as her
weight in the world.
