Abstract
We propose a method for morphological analysis and disambiguation for Kazakh language that accounts for both inflectional and derivational morphology, including not fully productive derivation. The method is data-driven and does not require manually generated rules. We leverage so called “transition chains” that help pruning false segmentations, while keeping correct ones. At the disambiguation step we use a standard HMM-based approach. Evaluating our method against open source solutions on several data sets, we show that it achieves better or on par performance. We also provide an extensive error analysis that sheds light on common problems of the morphological disambiguation of the language.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 151-163 |
| Number of pages | 13 |
| Volume | 9041 |
| ISBN (Print) | 9783319181103 |
| DOIs | |
| Publication status | Published - 2015 |
| Event | 16th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2015 - Cairo, Egypt Duration: Apr 14 2015 → Apr 20 2015 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9041 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 16th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2015 |
|---|---|
| Country | Egypt |
| City | Cairo |
| Period | 4/14/15 → 4/20/15 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Data-driven morphological analysis and disambiguation for Kazakh. / Makhambetov, Olzhas; Makazhanov, Aibek; Sabyrgaliyev, Islam; Yessenbayev, Zhandos.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9041 Springer Verlag, 2015. p. 151-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9041).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Data-driven morphological analysis and disambiguation for Kazakh
AU - Makhambetov, Olzhas
AU - Makazhanov, Aibek
AU - Sabyrgaliyev, Islam
AU - Yessenbayev, Zhandos
PY - 2015
Y1 - 2015
N2 - We propose a method for morphological analysis and disambiguation for Kazakh language that accounts for both inflectional and derivational morphology, including not fully productive derivation. The method is data-driven and does not require manually generated rules. We leverage so called “transition chains” that help pruning false segmentations, while keeping correct ones. At the disambiguation step we use a standard HMM-based approach. Evaluating our method against open source solutions on several data sets, we show that it achieves better or on par performance. We also provide an extensive error analysis that sheds light on common problems of the morphological disambiguation of the language.
AB - We propose a method for morphological analysis and disambiguation for Kazakh language that accounts for both inflectional and derivational morphology, including not fully productive derivation. The method is data-driven and does not require manually generated rules. We leverage so called “transition chains” that help pruning false segmentations, while keeping correct ones. At the disambiguation step we use a standard HMM-based approach. Evaluating our method against open source solutions on several data sets, we show that it achieves better or on par performance. We also provide an extensive error analysis that sheds light on common problems of the morphological disambiguation of the language.
UR - http://www.scopus.com/inward/record.url?scp=84942574744&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84942574744&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-18111-0_12
DO - 10.1007/978-3-319-18111-0_12
M3 - Conference contribution
SN - 9783319181103
VL - 9041
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 151
EP - 163
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -