A Combined Method Based on Stochastic and Linguistic Paradigm for the Understanding of Arabic Spontaneous Utterances

Lhioui, Chahira; Zouaghi, Anis; Zrigui, Mounir

doi:10.1007/978-3-642-37256-8_45

Cited by 9 publications

(8 citation statements)

References 4 publications

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“…In order to evaluate our system we made an experiment using a corpus containing three domain; train reservation as the single domain used in [5] and we add to other domain; Book request in a library and touristic information as done in [9].…”

Section: Tests and Resultsmentioning

confidence: 99%

Statistical Approach for Spontaneous Arabic Speech Understanding Based on Stochastic Speech Recognition Module

Trigui¹,

Terbeh²,

Zrigui³

2016

RCS

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This work is part of a big research project named "Oreillodule" aimed to develop tools for automatic speech recognition, translation, and synthesis for Arabic language. In this paper, our attention has mainly been focused on an attempt to present the semantic analyzer developed for the automatic comprehension of the standard spontaneous Arabic speech. We present a model of Arabic speech understanding system. In this model, both speech recognition module and semantic decoding module are based on statistical approach. In this work, we present and evaluate speech recognition module but we just explain the principle of Arabic speech understanding module.

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Section: Tests and Resultsmentioning

confidence: 99%

Statistical Approach for Spontaneous Arabic Speech Understanding Based on Stochastic Speech Recognition Module

Trigui¹,

Terbeh²,

Zrigui³

2016

RCS

Self Cite

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“…[23] are used utterances semantic labelling based on the frame grammar formalism. [24] are used syntactic parser context free grammar with HHM. [5] are Conditional Random Fields (CRF) to semantically label spoken Tunisian dialect turns.…”

Section: Related Workmentioning

confidence: 99%

Towards Understanding Egyptian Arabic Dialogues

Elmadany¹,

Abdou²,

Gheith³

2015

IJCA

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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.

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“…In this paper, we focus on language understanding component for Arabic dialogues system. However, there are few works have developed for Arabic spoken dialogue system either MSA or dialect as the best of our knowledge; this is mainly due to the lack of tools and resources that are necessary for the development of such systems (Zaghouani, 2014;Lhioui et al, 2013). Therefore, building language-understanding component for dialogue system is requiring four parts: (1) Dialogue Acts Annotation Schema (2) Dialogue corpus (3) Segmentation Classification (4) Dialogue Acts Classification; consequently, this paper present a survey for these parts.…”

Section: Natural Language Generation (Nlg)mentioning

confidence: 99%

“…Moreover, they used two machine-learning algorithms, Naïve Bayes and Decision Trees to induce classifiers acts for Arabic texts and they reported 41.73% as accuracy scores of all models. (Lhioui et al, 2013) proposed an approach based on syntactic parser for the proper treatment of utterances including certain phenomena such as ellipses and it has relies on the use of rule-base (context free grammar augmented with probabilities associated with rules) as show in Figure 6. In addition, they used HHM for creating the stochastic model (if a pretreated and transcribed sequence of words -this words are obviously the output of recognition module -and their annotated corresponding sequences was taken).…”

Section: 4arabic Dialogue Acts Classificationmentioning

confidence: 99%

A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message

Elmadany¹,

Abdou²,

Gheith³

2015

IJNLC

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Building dialogues systems interaction has recently gained considerable attention, but most of the resources and systems built so far are tailored to English and other Indo-European languages. The need for designing systems for other languages is increasing such as Arabic language. For this reasons, there are more interest for Arabic dialogue acts classification task because it a key player in Arabic language understanding to building this systems. This paper surveys different techniques for dialogue acts classification for Arabic. We describe the main existing techniques for utterances segmentations and classification, annotation schemas, and test corpora for Arabic dialogues understanding that have introduced in the literature

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A Combined Method Based on Stochastic and Linguistic Paradigm for the Understanding of Arabic Spontaneous Utterances

Cited by 9 publications

References 4 publications

Statistical Approach for Spontaneous Arabic Speech Understanding Based on Stochastic Speech Recognition Module

Statistical Approach for Spontaneous Arabic Speech Understanding Based on Stochastic Speech Recognition Module

Towards Understanding Egyptian Arabic Dialogues

A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message

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