Improving Arabic Stemmer: ISRI Stemmer

Stemmer is used in several types of applications such as Text Mining, Information Retrieval (IR), and Natural Language Processing (NLP). Stemmer is a step used to process text data. The main task in the stemmer is to return the word-formation to the basic word (root or stem). ISRI Stemmer is one of the Arabic stemmers contained in the NLTK package. This study improves the weakness of the ISRI stemmer in processing words consisting of two letters. From the results of the experiment, these improvements increased the stemmer yield by 7.3%

Unsupervised learning of Arabic non-concatenative morphology

Unsupervised approaches to learning the morphology of a language play an important role in computer processing of language from a practical and theoretical perspective, due their minimal reliance on manually produced linguistic resources and human annotation. Such approaches have been widely researched for the problem of concatenative affixation, but less attention has been paid to the intercalated (non-concatenative) morphology exhibited by Arabic and other Semitic languages. The aim of this research is to learn the root and pattern morphology of Arabic, with accuracy comparable to manually built morphological analysis systems. The approach is kept free from human supervision or manual parameter settings, assuming only that roots and patterns intertwine to form a word. Promising results were obtained by applying a technique adapted from previous work in concatenative morphology learning, which uses machine learning to determine relatedness between words. The output, with probabilistic relatedness values between words, was then used to rank all possible roots and patterns to form a lexicon. Analysis using trilateral roots resulted in correct root identification accuracy of approximately 86% for inflected words. Although the machine learning-based approach is effective, it is conceptually complex. So an alternative, simpler and computationally efficient approach was then devised to obtain morpheme scores based on comparative counts of roots and patterns. In this approach, root and pattern scores are defined in terms of each other in a mutually recursive relationship, converging to an optimized morpheme ranking. This technique gives slightly better accuracy while being conceptually simpler and more efficient. The approach, after further enhancements, was evaluated on a version of the Quranic Arabic Corpus, attaining a final accuracy of approximately 93%. A comparative evaluation shows this to be superior to two existing, well used manually built Arabic stemmers, thus demonstrating the practical feasibility of unsupervised learning of non-concatenative morphology

Nomenclature and Benchmarking Models of Text Classification Models: Contemporary Affirmation of the Recent Literature

In this paper we present automated text classification in text mining that is gaining greater relevance in various fields every day Text mining primarily focuses on developing text classification systems able to automatically classify huge volume of documents comprising of unstructured and semi structured data The process of retrieval classification and summarization simplifies extract of information by the user The finding of the ideal text classifier feature generator and distinct dominant technique of feature selection leading all other previous research has received attention from researchers of diverse areas as information retrieval machine learning and the theory of algorithms To automatically classify and discover patterns from the different types of the documents 1 techniques like Machine Learning Natural Language Processing NLP and Data Mining are applied together In this paper we review some effective feature selection researches and show the results in a table for

Applications of Mining Arabic Text: A Review

Since the appearance of text mining, the Arabic language gained some interest in applying several text mining tasks over a text written in the Arabic language. There are several challenges faced by the researchers. These tasks include Arabic text summarization, which is one of the challenging open areas for research in natural language processing (NLP) and text mining fields, Arabic text categorization, and Arabic sentiment analysis. This chapter reviews some of the past and current researches and trends in these areas and some future challenges that need to be tackled. It also presents some case studies for two of the reviewed approaches

Sentiment analysis of dialectical Arabic social media content using a hybrid linguistic-machine learning approach

Despite the enormous increase in the number of Arabic posts on social networks, the sentiment analysis research into extracting opinions from these posts lags behind that for the English language. This is largely attributed to the challenges in processing the morphologically complex Arabic natural language and the scarcity of Arabic NLP tools and resources. This complex task is further exacerbated when analysing dialectal Arabic that do not abide by the formal grammatical structure. Based on the semantic modelling of the target domain’s knowledge and multi-factor lexicon-based sentiment analysis, the intent of this research is to use a hybrid approach, integrating linguistic and machine learning methods for sentiment analysis classification of dialectal Arabic. First, a dataset of dialectal Arabic tweets was collected focusing on the unemployment domain, which is annotated manually. The tweets cover different dialectal Arabic in Saudi Arabia for which a comprehensive Arabic sentiment lexicon was constructed. This approach to sentiment analysis also integrated a novel light stemming mechanism towards improved Saudi dialectal Arabic stemming. Subsequently, a novel multi-factor lexicon-based sentiment analysis algorithm was developed for domain-specific social media posts written in dialectal Arabic. The algorithm considers several factors (emoji, intensifiers, negations, supplications) to improve the accuracy of the classifications. Applying this model to a central problem of sentiment analysis in dialectical Arabic, these operational techniques were deployed in order to assess analytical performance across social media channels which are vulnerable to semantic and colloquial variations. Finally, this study presented a new hybrid approach to sentiment analysis where domain knowledge is utilised in two methods to combine computational linguistics and machine learning; the first method integrates the problem domain semantic knowledgebase in the machine learning training features set, while the second uses the outcome of the lexicon-based sentiment classification in the training of the machine learning methods. By integrating these techniques into a single, hybridised solution, a greater degree of accuracy and consistency was achieved than applying each approach independently, confirming a pragmatic solution to sentiment classification in dialectical Arabic text

Sentiment Analysis for micro-blogging platforms in Arabic

Sentiment Analysis (SA) concerns the automatic extraction and classification of sentiments conveyed in a given text, i.e. labelling a text instance as positive, negative or neutral. SA research has attracted increasing interest in the past few years due to its numerous real-world applications. The recent interest in SA is also fuelled by the growing popularity of social media platforms (e.g. Twitter), as they provide large amounts of freely available and highly subjective content that can be readily crawled. Most previous SA work has focused on English with considerable success. In this work, we focus on studying SA in Arabic, as a less-resourced language. This work reports on a wide set of investigations for SA in Arabic tweets, systematically comparing three existing approaches that have been shown successful in English. Specifically, we report experiments evaluating fully-supervised-based (SL), distantsupervision- based (DS), and machine-translation-based (MT) approaches for SA. The investigations cover training SA models on manually-labelled (i.e. in SL methods) and automatically-labelled (i.e. in DS methods) data-sets. In addition, we explored an MT-based approach that utilises existing off-the-shelf SA systems for English with no need for training data, assessing the impact of translation errors on the performance of SA models, which has not been previously addressed for Arabic tweets. Unlike previous work, we benchmark the trained models against an independent test-set of >3.5k instances collected at different points in time to account for topic-shifts issues in the Twitter stream. Despite the challenging noisy medium of Twitter and the mixture use of Dialectal and Standard forms of Arabic, we show that our SA systems are able to attain performance scores on Arabic tweets that are comparable to the state-of-the-art SA systems for English tweets. The thesis also investigates the role of a wide set of features, including syntactic, semantic, morphological, language-style and Twitter-specific features. We introduce a set of affective-cues/social-signals features that capture information about the presence of contextual cues (e.g. prayers, laughter, etc.) to correlate them with the sentiment conveyed in an instance. Our investigations reveal a generally positive impact for utilising these features for SA in Arabic. Specifically, we show that a rich set of morphological features, which has not been previously used, extracted using a publicly-available morphological analyser for Arabic can significantly improve the performance of SA classifiers. We also demonstrate the usefulness of languageindependent features (e.g. Twitter-specific) for SA. Our feature-sets outperform results reported in previous work on a previously built data-set

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

The European Language Resources and Technologies Forum: Shaping the Future of the Multilingual Digital Europe

Proceedings of the 1st FLaReNet Forum on the European Language Resources and Technologies, held in Vienna, at the Austrian Academy of Science, on 12-13 February 2009