21 research outputs found
An automatic diacritization algorithm for undiacritized Arabic text
Modern Standard Arabic (MSA) is used today in most written and some spoken media. It is, however, not the native dialect of any country. Recently, the rate of the written dialectal Arabic text increased dramatically. Most of these texts have been written in the Egyptian dialectal, as it is considered the most widely used dialect and understandable throughout the Middle East. Like other Semitic languages, in written Arabic, short vowels are not written, but are represented by diacritic marks.
Nonetheless, these marks are not used in most of the modern Arabic texts (for example books and newspapers). The absence of diacritic marks creates a huge ambiguity, as the un-diacritized word may correspond to more than one correct
diacritization (vowelization) form. Hence, the aim of this research is to reduce the ambiguity of the absences of diacritic marks using hybrid algorithm with significantly higher accuracy than the state-of-the-art systems for MSA. Moreover, this research is to implement and evaluate the accuracy of the algorithm for dialectal Arabic text. The design of the proposed algorithm based on two main techniques as follows: statistical n-gram along with maximum likelihood estimation and morphological analyzer. Merging the word, morpheme, and letter levels with their sub-models together into one platform in order to improve the automatic
diacritization accuracy is the proposition of this research. Moreover, by utilizing the
feature of the case ending diacritization, which is ignoring the diacritic mark on the last letter of the word, shows a significant error improvement. The reason for this remarkable improvement is that the Arabic language prohibits adding diacritic marks over some letters. The hybrid algorithm demonstrated a good performance of 97.9% when applied to MSA corpora (Tashkeela), 97.1% when applied on LDC’s Arabic Treebank-Part 3 v1.0 and 91.8% when applied to Egyptian dialectal corpus (CallHome). The main contribution of this research is the hybrid algorithm for automatic diacritization of undiacritized MSA text and dialectal Arabic text. The proposed algorithm applied and evaluated on Egyptian colloquial dialect, the most widely dialect understood and used throughout the Arab world, which is considered
as first time based on the literature review
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
Multi-dialect Arabic broadcast speech recognition
Dialectal Arabic speech research suffers from the lack of labelled resources and
standardised orthography. There are three main challenges in dialectal Arabic
speech recognition: (i) finding labelled dialectal Arabic speech data, (ii) training
robust dialectal speech recognition models from limited labelled data and (iii)
evaluating speech recognition for dialects with no orthographic rules. This thesis
is concerned with the following three contributions:
Arabic Dialect Identification: We are mainly dealing with Arabic speech
without prior knowledge of the spoken dialect. Arabic dialects could be sufficiently
diverse to the extent that one can argue that they are different languages
rather than dialects of the same language. We have two contributions:
First, we use crowdsourcing to annotate a multi-dialectal speech corpus collected
from Al Jazeera TV channel. We obtained utterance level dialect labels for 57
hours of high-quality consisting of four major varieties of dialectal Arabic (DA),
comprised of Egyptian, Levantine, Gulf or Arabic peninsula, North African or
Moroccan from almost 1,000 hours. Second, we build an Arabic dialect identification
(ADI) system. We explored two main groups of features, namely acoustic
features and linguistic features. For the linguistic features, we look at a wide
range of features, addressing words, characters and phonemes. With respect to
acoustic features, we look at raw features such as mel-frequency cepstral coefficients
combined with shifted delta cepstra (MFCC-SDC), bottleneck features and
the i-vector as a latent variable. We studied both generative and discriminative
classifiers, in addition to deep learning approaches, namely deep neural network
(DNN) and convolutional neural network (CNN). In our work, we propose Arabic
as a five class dialect challenge comprising of the previously mentioned four
dialects as well as modern standard Arabic.
Arabic Speech Recognition: We introduce our effort in building Arabic automatic
speech recognition (ASR) and we create an open research community
to advance it. This section has two main goals: First, creating a framework for
Arabic ASR that is publicly available for research. We address our effort in building
two multi-genre broadcast (MGB) challenges. MGB-2 focuses on broadcast
news using more than 1,200 hours of speech and 130M words of text collected
from the broadcast domain. MGB-3, however, focuses on dialectal multi-genre
data with limited non-orthographic speech collected from YouTube, with special
attention paid to transfer learning. Second, building a robust Arabic ASR system
and reporting a competitive word error rate (WER) to use it as a potential
benchmark to advance the state of the art in Arabic ASR. Our overall system is
a combination of five acoustic models (AM): unidirectional long short term memory
(LSTM), bidirectional LSTM (BLSTM), time delay neural network (TDNN),
TDNN layers along with LSTM layers (TDNN-LSTM) and finally TDNN layers
followed by BLSTM layers (TDNN-BLSTM). The AM is trained using purely
sequence trained neural networks lattice-free maximum mutual information (LFMMI).
The generated lattices are rescored using a four-gram language model
(LM) and a recurrent neural network with maximum entropy (RNNME) LM.
Our official WER is 13%, which has the lowest WER reported on this task.
Evaluation: The third part of the thesis addresses our effort in evaluating dialectal
speech with no orthographic rules. Our methods learn from multiple
transcribers and align the speech hypothesis to overcome the non-orthographic
aspects. Our multi-reference WER (MR-WER) approach is similar to the BLEU
score used in machine translation (MT). We have also automated this process
by learning different spelling variants from Twitter data. We mine automatically
from a huge collection of tweets in an unsupervised fashion to build more than
11M n-to-m lexical pairs, and we propose a new evaluation metric: dialectal
WER (WERd). Finally, we tried to estimate the word error rate (e-WER) with
no reference transcription using decoding and language features. We show that
our word error rate estimation is robust for many scenarios with and without the
decoding features
Ensemble Morphosyntactic Analyser for Classical Arabic
Classical Arabic (CA) is an influential language for Muslim lives around the
world. It is the language of two sources of Islamic laws: the Quran and the Sunnah,
the collection of traditions and sayings attributed to the prophet Mohammed.
However, classical Arabic in general, and the Sunnah, in particular, is underexplored and under-resourced in the field of computational linguistics. This study examines the possible directions for adapting existing tools, specifically morphological analysers, designed for modern standard Arabic (MSA) to classical Arabic.
Morphological analysers of CA are limited, as well as the data for evaluating them. In this study, we adapt existing analysers and create a validation data-set from
the Sunnah books. Inspired by the advances in deep learning and the promising
results of ensemble methods, we developed a systematic method for transferring
morphological analysis that is capable of handling different labelling systems and
various sequence lengths.
In this study, we handpicked the best four open access MSA morphological analysers. Data generated from these analysers are evaluated before and after adaptation through the existing Quranic Corpus and the Sunnah Arabic Corpus. The findings are as follows: first, it is feasible to analyse under-resourced languages using existing comparable language resources given a small sufficient set of annotated text. Second, analysers typically generate different errors and this could be exploited. Third, an explicit alignment of sequences and the mapping of labels is not necessary to achieve comparable accuracies given a sufficient size of training dataset.
Adapting existing tools is easier than creating tools from scratch. The resulting quality is dependent on training data size and number and quality of input taggers. Pipeline architecture performs less well than the End-to-End neural network architecture due to error propagation and limitation on the output format. A valuable tool and data for annotating classical Arabic is made freely available
Statistical Parsing by Machine Learning from a Classical Arabic Treebank
Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic.
Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i’rāb (إعغاة ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis, dropped pronouns and hybrid representations.
A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic.
The Quran was chosen for annotation as a large body of work exists providing detailed syntactic analysis. Volunteer crowdsourcing is used for annotation in combination with expert supervision. A practical result of the annotation effort is the corpus website: http://corpus.quran.com, an educational resource with over two million users per year
A Computational Lexicon and Representational Model for Arabic Multiword Expressions
The phenomenon of multiword expressions (MWEs) is increasingly recognised as a serious and challenging issue that has attracted the attention of researchers in various language-related disciplines. Research in these many areas has emphasised the primary role of MWEs in the process of analysing and understanding language, particularly in the computational treatment of natural languages. Ignoring MWE knowledge in any NLP system reduces the possibility of achieving high precision outputs. However, despite the enormous wealth of MWE research and language resources available for English and some other languages, research on Arabic MWEs (AMWEs) still faces multiple challenges, particularly in key computational tasks such as extraction, identification, evaluation, language resource building, and lexical representations.
This research aims to remedy this deficiency by extending knowledge of AMWEs and making noteworthy contributions to the existing literature in three related research areas on the way towards building a computational lexicon of AMWEs. First, this study develops a general understanding of AMWEs by establishing a detailed conceptual framework that includes a description of an adopted AMWE concept and its distinctive properties at multiple linguistic levels. Second, in the use of AMWE extraction and discovery tasks, the study employs a hybrid approach that combines knowledge-based and data-driven computational methods for discovering multiple types of AMWEs. Third, this thesis presents a representative system for AMWEs which consists of multilayer encoding of extensive linguistic descriptions.
This project also paves the way for further in-depth AMWE-aware studies in NLP and linguistics to gain new insights into this complicated phenomenon in standard Arabic. The implications of this research are related to the vital role of the AMWE lexicon, as a new lexical resource, in the improvement of various ANLP tasks and the potential opportunities this lexicon provides for linguists to analyse and explore AMWE phenomena