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2020 ivannikov Memorial Workshop, IVMEM 2020

作者： Malajyan, Arthur Avetisyan, Karen Ghukasyan, Tsolak Ivannikov Laboratory for System Programming Russian-Armenian University Yerevan Armenia

ISBN: (纸本)9781728190884

In this work, we employ a semi-automatic method based on back translation to generate a sentential paraphrase corpus for the Armenian language. The initial collection of sentences is translated from Armenian to English and back twice, resulting in pairs of lexically distant but semantically similar sentences. The generated paraphrases are then manually reviewed and annotated. Using the method train and test datasets are created, containing 2360 paraphrases in total. In addition, the datasets are used to train and evaluate BERT-based models for detecting paraphrase in Armenian, achieving results comparable to the state-of-the-art of other languages. © 2020 IEEE.

关键词： Translation (languages)

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LingvoDoc: Phonology Option Features (Using theExample of the Forest Nenets Languages)

LingvoDoc: Phonology Option Features (Using theExample of th...

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2023 ivannikov ISPRAS Open Conference, ISPRAS 2023

作者： Koshelyuk, Natalia Ivannikov Institute for System Programming of the Ras Linguistic Platforms Laboratory Moscow Russia

ISBN: (纸本)9798350349993

The article presents the results of a study of the field material of the Purovsky dialect of the Forest Nenets language. His experimental phonological analysis was conducted using the LingvoDoc data processing algorithms. A comparison of the vowel sound system obtained as a result of this analysis with other phonetic systems of the Forest Nenets language proposed by linguists of the XX-XXI centuries, and its four separate native speakers, allowed us to conclude that this dialect is unique and further research is necessary, taking into account data on contact languages for the Forest Nenets. © 2023 IEEE.

关键词： Data handling

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The Position of the Upper Kama Dialect in the Komi Language Dialectal Area: Evidence from the LingvoDoc Platform

The Position of the Upper Kama Dialect in the Komi Language ...

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2023 ivannikov ISPRAS Open Conference, ISPRAS 2023

作者： Bazhenova, Olga Ivannikov Institute for System Programming of the Russian Academy of Sciences 'Linguistic Platforms' Laboratory Moscow Russia

ISBN: (纸本)9798350349993

The Upper Kama dialect is spoken by the Komi ethnic group residing in the upper Kama River region. The idiom developed independently from the area of distribution of Komi dialects and existed solely in oral form. In Komi linguistics, there is debate regarding the position of the Upper Kama idiom within the dialectal area of the Komi language. The paper presents the main stages of data processing for four Komi-Permyak dictionaries, including two archival dictionaries from the turn of the 19th-20th centuries, two audio dictionaries of the southern Komi-Zyryan dialects, and an audio dictionary of the Upper Kama dialect uploaded on the LingvoDoc platform. The study found that, based on phonetic-etymological criteria, the Upper Kama dialect is the most similar to the modern Upper Sysola dialect of the Komi-Zyryan language. However, when analyzing the similarity of the basic vocabulary, the dialect in question is most similar to the Kudymkar dialect of the southern Komi-Permyak language. This contradiction may explain the numerous discussions about the position of the Upper Kama dialect in the Komi language dialectal area. © 2023 IEEE.

关键词： Data handling

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Classification of the Stavropol Dialect of the Estonian Language Based on Field Record Analysis Using Digital Methods

Classification of the Stavropol Dialect of the Estonian Lang...

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2023 ivannikov ISPRAS Open Conference, ISPRAS 2023

作者： Dogadushkin, D. Normankaja, J. Ivannikov Institute for System Programming of the Russian Academy of Sciences 'Linguistic Platforms' Laboratory Moscow Russia

ISBN: (纸本)9798350349993

The question of accurate classification of a language or a dialect becomes particularly relevant when its speakers, due to migrations, find themselves in isolated regions. Geographically distant from their original places of residence. This prompts the inquiry: do these dialects more often succumb to the influence of nearby languages or do they, conversely, 'preserve' themselves with minimal changes? Within the scope of the current research we have aimed to answer this question for a unique Stavropol dialect of the Estonian language, that has been isolated near northern Caucasus for almost 200 years. In 2020 we have recorded a full dictionary of the supposed dialect, containing 2583 entries. Through the examination of this material from three dictionaries using the 'Cognate analysis' (It calculates the phonetic disparities in etymologically related words on LingvoDoc) and the 'Glottochronological Analysis of Dialects' programs on LingvoDoc we've established that Thus, the language of the Podgornoye village exhibits both innovative reflexes (loss of the initial h) and archaic reflexes in consonants. In addition, with regard to vowels, it displays both more archaic features (retention of diphthongs uo and ie) and innovative reflexes (for Estonian diphthongs ea and ó ó). This corroborates with our the hypothesis regarding the 'conservation' of the Stavropol dialect of the Estonian language when compared to standard Estonian. Consequently, on the etymological-phonetic proximity graph, the language of the Podgornoye village is closer to Finnish than Estonian. Thus the Estonian dialect of the village of Podgornoye serves as an example of almost complete 'conservation' after the migration of its speakers to the North Caucasus, where the unique features of Northern Estonian dialects have been preserved through active language use by its speakers. In conclusion, we can see that the lists of basic vocabulary of the dialect of the village of Podgornoye and of the literary Esto

关键词： Neurophysiology

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pioNER: Datasets and baselines for Armenian named entity recognition

arXiv

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arXiv 2018年

作者： Ghukasyan, Tsolak Davtyan, Garnik Avetisyan, Karen Andrianov, Ivan Ivannikov Laboratory for System Programming Russian-Armenian University Yerevan Armenia Ivannikov Institute for System Programming Russian Academy of Sciences Moscow Russia

In this work, we tackle the problem of Armenian named entity recognition, providing silver- and gold-standard datasets as well as establishing baseline results on popular models. We present a 163000-token named entity corpus automatically generated and annotated from Wikipedia, and another 53400-token corpus of news sentences with manual annotation of people, organization and location named entities. The corpora were used to train and evaluate several popular named entity recognition models. Alongside the datasets, we release 50-, 100-, 200-, 300-dimensional GloVe word embeddings trained on a collection of Armenian texts from Wikipedia, news, blogs, and encyclopedia. Copyright © 2018, The Authors. All rights reserved.

关键词： Natural language processing systems

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Comparative analysis of non/ductedpropeller under the influence of vertical wall

Comparative analysis of non/ductedpropeller under the influe...

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2022 ivannikov Open Conference, ISPRAS 2022

作者： Wang, Mengfei Qingxu Zhang, Qin Epikhin, Andrey Liang, Bingchen Ocean University of China College of Engineering Qingdao China Programming of the Ras Ivannikov Institute for System Moscow Russia Shandong Provincial Key Laboratory of Ocean Engineering Qingdao China

ISBN: (纸本)9798350398533

A numerical and experimental study has been conducted to explore the influence of vertical wall on the wake of the non/ducted propeller. The numerical simulation is achieved using a structured and transient sliding mesh approach. The control equations are closed by the Delayed Detached Eddy Simulation (DDES), and the computational results are compared with the available experimental data. By dividing the ducted propeller flow wake vortex shape into different behavior regions, the temporal vortex evolution near the wake of the propellers under the influence of different vertical walls are emphatically analysed. The results show that the jet flow wake reaches the wall, forming a conical low-speed area at the centre of the wall. The fluid circulates from the outer side to the inner side of the cone, forming a vortex whose size first increases and then decreases with the distance between the propellers and the wall. In addition, the change of propellers wake velocity profile is given. The wake velocity of propeller and ducted propeller decreases exponentially and linearly along the flow direction, respectively. © 2022 IEEE.

关键词： Wakes

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ARPA: Armenian Paraphrase Detection Corpus and Models

ARPA: Armenian Paraphrase Detection Corpus and Models

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ivannikov Memorial Workshop (IVMEM)

作者： Arthur Malajyan Karen Avetisyan Tsolak Ghukasyan Ivannikov Laboratory for System Programming at Russian-Armenian University Yerevan Armenia

ISBN: (数字)9781728190884

ISBN: (纸本)9781728190891

关键词： Conferences

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ARPA: Armenian paraphrase detection corpus and models

arXiv

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arXiv 2020年

作者： Malajyan, Arthur Avetisyan, Karen Ghukasyan, Tsolak Ivannikov Laboratory for System Programming at Russian-Armenian University Yerevan Armenia

关键词： Machine learning

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Word Embeddings for the Armenian Language: Intrinsic and Extrinsic Evaluation

arXiv

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arXiv 2019年

作者： Avetisyan, Karen Ghukasyan, Tsolak Ivannikov Laboratory for System Programming at Russian-Armenian University Yerevan Armenia

In this work, we intrinsically and extrinsically evaluate and compare existing word embedding models for the Armenian language. Alongside, new embeddings are presented, trained using GloVe, fastText, CBOW, SkipGram algorithms. We adapt and use the word analogy task in intrinsic evaluation of embeddings. For extrinsic evaluation, two tasks are employed: morphological tagging and text classification. Tagging is performed on a deep neural network, using ArmTDP v2.3 dataset. For text classification, we propose a corpus of news articles categorized into 7 classes. The datasets are made public to serve as benchmarks for future models. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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LingvoDoc: Phonology Option Features (Using theExample of the Forest Nenets Languages)

LingvoDoc: Phonology Option Features (Using theExample of th...

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ivannikov ISPRAS Open Conference (ISPRAS)

作者： Natalia Koshelyuk Linguistic Platforms Laboratory Ivannikov Institute for System Programming of the RAS Moscow Russia

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