Standard

Automatic Word Clustering in Russian Texts. / Mitrofanova, O.; Mukhin, Anton; Panicheva, P.; Savitsky, V.

Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings. Springer Nature, 2007. стр. 85-97 (Lecture Notes in Computer Science; Том 4629).

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

Harvard

Mitrofanova, O, Mukhin, A, Panicheva, P & Savitsky, V 2007, Automatic Word Clustering in Russian Texts. в Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings. Lecture Notes in Computer Science, Том. 4629, Springer Nature, стр. 85-97, 10th International Conference , Pilsen, Чехия, 3/09/07.

APA

Mitrofanova, O., Mukhin, A., Panicheva, P., & Savitsky, V. (2007). Automatic Word Clustering in Russian Texts. в Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings (стр. 85-97). (Lecture Notes in Computer Science; Том 4629). Springer Nature.

Vancouver

Mitrofanova O, Mukhin A, Panicheva P, Savitsky V. Automatic Word Clustering in Russian Texts. в Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings. Springer Nature. 2007. стр. 85-97. (Lecture Notes in Computer Science).

Author

Mitrofanova, O. ; Mukhin, Anton ; Panicheva, P. ; Savitsky, V. / Automatic Word Clustering in Russian Texts. Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings. Springer Nature, 2007. стр. 85-97 (Lecture Notes in Computer Science).

BibTeX

@inproceedings{7752b2f2a32a44d79d50a5daeb0055dc,
title = "Automatic Word Clustering in Russian Texts",
abstract = "The paper deals with development and application of automatic word clustering (AWC) tool aimed at processing Russian texts of various types, which should satisfy the requirements of flexibility and compatibility with other linguistic resources. The construction of AWC tool requires computer implementation of latent semantic analysis (LSA) combined with clustering algorithms. To meet the need, Python-based software has been developed. Major procedures performed by AWC tool are segmentation of input texts and context analysis, co-occurrence matrix construction, agglomerative and K-means clustering. Special attention is drawn to experimental results on clustering words in raw texts with changing parameters.",
author = "O. Mitrofanova and Anton Mukhin and P. Panicheva and V. Savitsky",
year = "2007",
language = "English",
isbn = "9783540746270",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "85--97",
booktitle = "Text, Speech and Dialogue",
address = "Germany",
note = "10th International Conference , TSD 2007 ; Conference date: 03-09-2007 Through 07-09-2007",

}

RIS

TY - GEN

T1 - Automatic Word Clustering in Russian Texts

AU - Mitrofanova, O.

AU - Mukhin, Anton

AU - Panicheva, P.

AU - Savitsky, V.

PY - 2007

Y1 - 2007

N2 - The paper deals with development and application of automatic word clustering (AWC) tool aimed at processing Russian texts of various types, which should satisfy the requirements of flexibility and compatibility with other linguistic resources. The construction of AWC tool requires computer implementation of latent semantic analysis (LSA) combined with clustering algorithms. To meet the need, Python-based software has been developed. Major procedures performed by AWC tool are segmentation of input texts and context analysis, co-occurrence matrix construction, agglomerative and K-means clustering. Special attention is drawn to experimental results on clustering words in raw texts with changing parameters.

AB - The paper deals with development and application of automatic word clustering (AWC) tool aimed at processing Russian texts of various types, which should satisfy the requirements of flexibility and compatibility with other linguistic resources. The construction of AWC tool requires computer implementation of latent semantic analysis (LSA) combined with clustering algorithms. To meet the need, Python-based software has been developed. Major procedures performed by AWC tool are segmentation of input texts and context analysis, co-occurrence matrix construction, agglomerative and K-means clustering. Special attention is drawn to experimental results on clustering words in raw texts with changing parameters.

UR - https://link.springer.com/chapter/10.1007/978-3-540-74628-7_13

M3 - Conference contribution

SN - 9783540746270

T3 - Lecture Notes in Computer Science

SP - 85

EP - 97

BT - Text, Speech and Dialogue

PB - Springer Nature

T2 - 10th International Conference

Y2 - 3 September 2007 through 7 September 2007

ER -

ID: 4509961