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 F 1 scores of 70.36% overall domains.

Arabic Dialogue Processing and Act Classification using Support Vector Machine

“…The automatic comprehension of Arabic dialogues for the Egyptian dialect at the utterance level is a challenge that addresses in the paper [15] utilized a machine learning approach, called YOSR. The extractor has been tested on a dataset of impromptu conversations and instant messages for the Egyptian dialect.…”

Issues of Dialectal Saudi Twitter Corpus

“…The Arabic language is a Semitic language and is the native tongue in 22 Arab countries. In addition, it is one of the six official languages of the United Nations [15,18]. Arabic consists of 29 letters that can be used to form words; other languages, such as Farsi and Urdu, also use Arabic characters [15].…”

“…In addition, it is one of the six official languages of the United Nations [15,18]. Arabic consists of 29 letters that can be used to form words; other languages, such as Farsi and Urdu, also use Arabic characters [15]. Although Arabic is a widely spoken language, it is a language with a very complex morphology owing to the fact that it is highly inflectional.…”