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. p. 85-97 (Lecture Notes in Computer Science; Vol. 4629).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research
Harvard
Mitrofanova, O, Mukhin, A
, Panicheva, P & Savitsky, V 2007,
Automatic Word Clustering in Russian Texts. in
Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings. Lecture Notes in Computer Science, vol. 4629, Springer Nature, pp. 85-97, 10th International Conference , Pilsen, Czech Republic,
3/09/07.
APA
Mitrofanova, O., Mukhin, A.
, Panicheva, P., & Savitsky, V. (2007).
Automatic Word Clustering in Russian Texts. In
Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings (pp. 85-97). (Lecture Notes in Computer Science; Vol. 4629). Springer Nature.
Vancouver
Mitrofanova O, Mukhin A
, Panicheva P, Savitsky V.
Automatic Word Clustering in Russian Texts. In Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings. Springer Nature. 2007. p. 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. pp. 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 -
