Fine-grain morphological analyzer and part-of-speech tagger for Arabic text

Morphological analyzers and part-of-speech taggers are key technologies for most text analysis applications. Our aim is to develop a part-of-speech tagger for annotating a wide range of Arabic text formats, domains and genres including both vowelized and non-vowelized text. Enriching the text with linguistic analysis will maximize the potential for corpus re-use in a wide range of applications. We foresee the advantage of enriching the text with part-of-speech tags of very fine-grained grammatical distinctions, which reflect expert interest in syntax and morphology, but not specific needs of end-users, because end-user applications are not known in advance. In this paper we review existing Arabic Part-of-Speech Taggers and tag-sets, and illustrate four different Arabic PoS tag-sets for a sample of Arabic text from the Quran. We describe the detailed fine-grained morphological feature tag set of Arabic, and the fine-grained Arabic morphological analyzer algorithm. We faced practical challenges in applying the morphological analyzer to the 100-million-word Web Arabic Corpus: we had to port the software to the National Grid Service, adapt the analyser to cope with spelling variations and errors, and utilise a Broad-Coverage Lexical Resource combining 23 traditional Arabic lexicons. Finally we outline the construction of a Gold Standard for comparative evaluation

Tools for Arabic Natural Language Processing: a case study in qalqalah prosody

In this paper, we focus on the prosodic effect of qalqalah or "vibration" applied to a subset of Arabic consonants under certain constraints during correct Qur'anic recitation or taǧwīd, using our Boundary-Annotated Qur’an dataset of 77430 words (Brierley et al 2012; Sawalha et al 2014). These qalqalah events are rule-governed and are signified orthographically in the Arabic script. Hence they can be given abstract definition in the form of regular expressions and thus located and collected automatically. High frequency qalqalah content words are also found to be statistically significant discriminators or keywords when comparing Meccan and Medinan chapters in the Qur'an using a state-of-the-art Visual Analytics toolkit: Semantic Pathways. Thus we hypothesise that qalqalah prosody is one way of highlighting salient items in the text. Finally, we implement Arabic transcription technology (Brierley et al under review; Sawalha et al forthcoming) to create a qalqalah pronunciation guide where each word is transcribed phonetically in IPA and mapped to its chapter-verse ID. This is funded research under the EPSRC "Working Together" theme

The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people’s opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which is required to label words according to grammatical categories. The challenge of analyzing the Arabic language has found considerable research interest, but now the challenge is amplified with the addition of social media dialects. While numerous morphological analyzers and PoS taggers were proposed for Modern Standard Arabic (MSA), we are now witnessing an increased interest in applying those techniques to the Arabic dialect that is prominent in social media. Indeed, social media texts (e.g. posts, comments, and replies) differ significantly from MSA texts in terms of vocabulary and grammatical structure. Such differences call for reviewing the PoS tagging methods to adapt social media texts. Furthermore, the lack of sufficiently large and diverse social media text corpora constitutes one of the reasons that automatic PoS tagging of social media content has been rarely studied. In this paper, we address those limitations by proposing a novel Arabic social media text corpus that is enriched with complete PoS information, including tags, lemmas, and synonyms. The proposed corpus constitutes the largest manually annotated Arabic corpus to date, with more than 5 million tokens, 238,600 MSA texts, and words from Arabic social media dialect, collected from 65,000 online users’ accounts. Furthermore, our proposed corpus was used to train a custom Long Short-Term Memory deep learning model and showed excellent performance in terms of sentiment classification accuracy and F1-score. The obtained results demonstrate that the use of a diverse corpus that is enriched with PoS information significantly enhances the performance of social media analysis techniques and opens the door for advanced features such as opinion mining and emotion intelligence

Corpus Linguistics Resources and Tools for Arabic Lexicography

ktb:[Alkitab] the book; is well known. The plural forms are [kutubun] and [kutbun].[kataba Alshay'] He wrote something.[yaktubuhu] the action of writing something.[katban],[kitaban] and [kitabatan] means the art of writing. And [kattabahu] writing it means draw it up. Abu Al-Najim said: I returned back from Ziyad's house [after meeting him] and behaved demented, my legs drawn up differently (means walking in a different way). They wrote [tukattibani] on the road the letters of Lam Alif (describing how he was walking crazily and in a different ..

Open-source resources and standards for Arabic word structure analysis: Fine grained morphological analysis of Arabic text corpora

Morphological analyzers are preprocessors for text analysis. Many Text Analytics applications need them to perform their tasks. The aim of this thesis is to develop standards, tools and resources that widen the scope of Arabic word structure analysis - particularly morphological analysis, to process Arabic text corpora of different domains, formats and genres, of both vowelized and non-vowelized text. We want to morphologically tag our Arabic Corpus, but evaluation of existing morphological analyzers has highlighted shortcomings and shown that more research is required. Tag-assignment is significantly more complex for Arabic than for many languages. The morphological analyzer should add the appropriate linguistic information to each part or morpheme of the word (proclitic, prefix, stem, suffix and enclitic); in effect, instead of a tag for a word, we need a subtag for each part. Very fine-grained distinctions may cause problems for automatic morphosyntactic analysis – particularly probabilistic taggers which require training data, if some words can change grammatical tag depending on function and context; on the other hand, finegrained distinctions may actually help to disambiguate other words in the local context. The SALMA – Tagger is a fine grained morphological analyzer which is mainly depends on linguistic information extracted from traditional Arabic grammar books and prior knowledge broad-coverage lexical resources; the SALMA – ABCLexicon. More fine-grained tag sets may be more appropriate for some tasks. The SALMA –Tag Set is a theory standard for encoding, which captures long-established traditional fine-grained morphological features of Arabic, in a notation format intended to be compact yet transparent. The SALMA – Tagger has been used to lemmatize the 176-million words Arabic Internet Corpus. It has been proposed as a language-engineering toolkit for Arabic lexicography and for phonetically annotating the Qur’an by syllable and primary stress information, as well as, fine-grained morphological tagging

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

Parallel corpus multi stream question answering with applications to the Qu'ran

Question-Answering (QA) is an important research area, which is concerned with developing an automated process that answers questions posed by humans in a natural language. QA is a shared task for the Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing communities (NLP). A technical review of different QA system models and methodologies reveals that a typical QA system consists of different components to accept a natural language question from a user and deliver its answer(s) back to the user. Existing systems have been usually aimed at structured/ unstructured data collected from everyday English text, i.e. text collected from television programmes, news wires, conversations, novels and other similar genres. Despite all up-to-date research in the subject area, a notable fact is that none of the existing QA Systems has been tested on a Parallel Corpus of religious text with the aim of question answering. Religious text has peculiar characteristics and features which make it more challenging for traditional QA methods than other kinds of text. This thesis proposes PARMS (Parallel Corpus Multi Stream) Methodology; a novel method applying existing advanced IR (Information Retrieval) techniques, and combining them with NLP (Natural Language Processing) methods and additional semantic knowledge to implement QA (Question Answering) for a parallel corpus. A parallel Corpus involves use of multiple forms of the same corpus where each form differs from others in a certain aspect, e.g. translations of a scripture from one language to another by different translators. Additional semantic knowledge can be referred as a stream of information related to a corpus. PARMS uses Multiple Streams of semantic knowledge including a general ontology (WordNet) and domain-specific ontologies (QurTerms, QurAna, QurSim). This additional knowledge has been used in embedded form for Query Expansion, Corpus Enrichment and Answer Ranking. The PARMS Methodology has wider applications. This thesis applies it to the Quran – the core text of Islam; as a first case study. The PARMS Method uses parallel corpus comprising ten different English translations of the Quran. An individual Quranic verse is treated as an answer to questions asked in a natural language, English. This thesis also implements PARMS QA Application as a proof of concept for the PARMS methodology. The PARMS Methodology aims to evaluate the range of semantic knowledge streams separately and in combination; and also to evaluate alternative subsets of the DATA source: QA from one stream vs. parallel corpus. Results show that use of Parallel Corpus and Multiple Streams of semantic knowledge have obvious advantages. To the best of my knowledge, this method is developed for the first time and it is expected to be a benchmark for further research area