Towards Understanding Egyptian Arabic Dialogues
Abstract
Labelling of user's utterances to understanding his attends which called Dialogue Act (DA) classification, it is considered the key player for dialogue language understanding layer in automatic dialogue systems. In this paper, we proposed a novel approach to user's utterances labeling for Egyptian spontaneous dialogues and Instant Messages using Machine Learning (ML) approach without relying on any special lexicons, cues, or rules. Due to the lack of Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus includes 4725 utterances for three domains, which are collected and annotated manually from Egyptian call-centers. The system achieves F1 scores of 70. 36% overall domains.
Cite
@article{arxiv.1509.03208,
title = {Towards Understanding Egyptian Arabic Dialogues},
author = {Abdelrahim A Elmadany and Sherif M Abdou and Mervat Gheith},
journal= {arXiv preprint arXiv:1509.03208},
year = {2015}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1505.03081