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Evaluating distributional semantic models with Russian noun-adjective compositions. / Panicheva, Polina; Protopopova, Ekaterina; Bukia, Grigoriy; Mitrofanova, Olga.

Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers. ред. / Natalia Loukachevitch; Alexander Panchenko; Konstantin Vorontsov; Valeri G. Labunets; Andrey V. Savchenko; Dmitry I. Ignatov; Sergey I. Nikolenko; Mikhail Yu. Khachay. Springer Nature, 2017. стр. 236-247 (Communications in Computer and Information Science; Том 661).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование

Harvard

Panicheva, P, Protopopova, E, Bukia, G & Mitrofanova, O 2017, Evaluating distributional semantic models with Russian noun-adjective compositions. в N Loukachevitch, A Panchenko, K Vorontsov, VG Labunets, AV Savchenko, DI Ignatov, SI Nikolenko & MY Khachay (ред.), Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers. Communications in Computer and Information Science, Том. 661, Springer Nature, стр. 236-247, 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016, Yekaterinburg, Российская Федерация, 7/04/16. https://doi.org/10.1007/978-3-319-52920-2_22

APA

Panicheva, P., Protopopova, E., Bukia, G., & Mitrofanova, O. (2017). Evaluating distributional semantic models with Russian noun-adjective compositions. в N. Loukachevitch, A. Panchenko, K. Vorontsov, V. G. Labunets, A. V. Savchenko, D. I. Ignatov, S. I. Nikolenko, & M. Y. Khachay (Ред.), Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers (стр. 236-247). (Communications in Computer and Information Science; Том 661). Springer Nature. https://doi.org/10.1007/978-3-319-52920-2_22

Vancouver

Panicheva P, Protopopova E, Bukia G, Mitrofanova O. Evaluating distributional semantic models with Russian noun-adjective compositions. в Loukachevitch N, Panchenko A, Vorontsov K, Labunets VG, Savchenko AV, Ignatov DI, Nikolenko SI, Khachay MY, Редакторы, Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers. Springer Nature. 2017. стр. 236-247. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-319-52920-2_22

Author

Panicheva, Polina ; Protopopova, Ekaterina ; Bukia, Grigoriy ; Mitrofanova, Olga. / Evaluating distributional semantic models with Russian noun-adjective compositions. Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers. Редактор / Natalia Loukachevitch ; Alexander Panchenko ; Konstantin Vorontsov ; Valeri G. Labunets ; Andrey V. Savchenko ; Dmitry I. Ignatov ; Sergey I. Nikolenko ; Mikhail Yu. Khachay. Springer Nature, 2017. стр. 236-247 (Communications in Computer and Information Science).

BibTeX

@inproceedings{03fbe27c04c14d61a2095f5e47ec885f,
title = "Evaluating distributional semantic models with Russian noun-adjective compositions",
abstract = "In the paper vector-space semantic models based on Word2Vec word embeddings algorithm and a count-based association-oriented algorithm are evaluated and compared by measuring association strength between Russian nouns and adjectives. A dataset of nouns and associated adjectives is used as the test set for pseudodisambiguation task. Models are trained with corpora of Russian fiction. A measure of lexical association anomaly is applied evaluating similarity between the initial noun and the resulting attributive phrase. Results of association strength are reported for models characterized by different parameter values; the best parameter value combinations are proposed. The test exemplars producing the error rate are manually annotated, and the model errors are categorized in terms of their linguistic nature and compositionality features.",
keywords = "Association measures, Distributional semantics, Selectional restrictions, Vector-space representation evaluation, Vector-space semantic models",
author = "Polina Panicheva and Ekaterina Protopopova and Grigoriy Bukia and Olga Mitrofanova",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-52920-2_22",
language = "English",
isbn = "9783319529196",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature",
pages = "236--247",
editor = "Natalia Loukachevitch and Alexander Panchenko and Konstantin Vorontsov and Labunets, {Valeri G.} and Savchenko, {Andrey V.} and Ignatov, {Dmitry I.} and Nikolenko, {Sergey I.} and Khachay, {Mikhail Yu.}",
booktitle = "Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers",
address = "Germany",
note = "5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016 ; Conference date: 07-04-2016 Through 09-04-2016",

}

RIS

TY - GEN

T1 - Evaluating distributional semantic models with Russian noun-adjective compositions

AU - Panicheva, Polina

AU - Protopopova, Ekaterina

AU - Bukia, Grigoriy

AU - Mitrofanova, Olga

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In the paper vector-space semantic models based on Word2Vec word embeddings algorithm and a count-based association-oriented algorithm are evaluated and compared by measuring association strength between Russian nouns and adjectives. A dataset of nouns and associated adjectives is used as the test set for pseudodisambiguation task. Models are trained with corpora of Russian fiction. A measure of lexical association anomaly is applied evaluating similarity between the initial noun and the resulting attributive phrase. Results of association strength are reported for models characterized by different parameter values; the best parameter value combinations are proposed. The test exemplars producing the error rate are manually annotated, and the model errors are categorized in terms of their linguistic nature and compositionality features.

AB - In the paper vector-space semantic models based on Word2Vec word embeddings algorithm and a count-based association-oriented algorithm are evaluated and compared by measuring association strength between Russian nouns and adjectives. A dataset of nouns and associated adjectives is used as the test set for pseudodisambiguation task. Models are trained with corpora of Russian fiction. A measure of lexical association anomaly is applied evaluating similarity between the initial noun and the resulting attributive phrase. Results of association strength are reported for models characterized by different parameter values; the best parameter value combinations are proposed. The test exemplars producing the error rate are manually annotated, and the model errors are categorized in terms of their linguistic nature and compositionality features.

KW - Association measures

KW - Distributional semantics

KW - Selectional restrictions

KW - Vector-space representation evaluation

KW - Vector-space semantic models

UR - http://www.scopus.com/inward/record.url?scp=85014236498&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-52920-2_22

DO - 10.1007/978-3-319-52920-2_22

M3 - Conference contribution

AN - SCOPUS:85014236498

SN - 9783319529196

T3 - Communications in Computer and Information Science

SP - 236

EP - 247

BT - Analysis of Images, Social Networks and Texts - 5th International Conference, AIST 2016, Revised Selected Papers

A2 - Loukachevitch, Natalia

A2 - Panchenko, Alexander

A2 - Vorontsov, Konstantin

A2 - Labunets, Valeri G.

A2 - Savchenko, Andrey V.

A2 - Ignatov, Dmitry I.

A2 - Nikolenko, Sergey I.

A2 - Khachay, Mikhail Yu.

PB - Springer Nature

T2 - 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016

Y2 - 7 April 2016 through 9 April 2016

ER -

ID: 47480880