Mоlimо vаs kоristitе оvај idеntifikаtоr zа citirаnjе ili оvај link dо оvе stаvkе:
https://open.uns.ac.rs/handle/123456789/814
Nаziv: | Morphology-based vs unsupervised word clustering for training language models for Serbian | Аutоri: | Ostrogonac S. Pakoci, Edvin Sečujski, Milan Mišković, Dragiša |
Dаtum izdаvаnjа: | 1-јан-2019 | Čаsоpis: | Acta Polytechnica Hungarica | Sažetak: | © 2019, Budapest Tech Polytechnical Institution. All rights reserved. When training language models (especially for highly inflective languages), some applications require word clustering in order to mitigate the problem of insufficient training data or storage space. The goal of word clustering is to group words that can be well represented by a single class in the sense of probabilities of appearances in different contexts. This paper presents comparative results obtained by using different approaches to word clustering when training class N-gram models for Serbian, as well as models based on recurrent neural networks. One approach is unsupervised word clustering based on optimized Brown’s algorithm, which relies on bigram statistics. The other approach is based on morphology, and it requires expert knowledge and language resources. Four different types of textual corpora were used in experiments, describing different functional styles. The language models were evaluated by both perplexity and word error rate. The results show notable advantage of introducing expert knowledge into word clustering process. | URI: | https://open.uns.ac.rs/handle/123456789/814 | ISSN: | 17858860 | DOI: | 10.12700/APH.16.2.2019.2.11 |
Nаlаzi sе u kоlеkciјаmа: | FTN Publikacije/Publications |
Prikаzаti cеlоkupаn zаpis stаvki
SCOPUSTM
Nаvоđеnjа
9
prоvеrеnо 03.05.2024.
Prеglеd/i stаnicа
23
Prоtеklа nеdеljа
0
0
Prоtеkli mеsеc
0
0
prоvеrеnо 10.05.2024.
Google ScholarTM
Prоvеritе
Аlt mеtrikа
Stаvkе nа DSpace-u su zаštićеnе аutоrskim prаvimа, sа svim prаvimа zаdržаnim, оsim аkо nije drugačije naznačeno.