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

Supervised Learning for Multi-Domain Text Classification

Digital information available on the Internet is increasing day by day. As a result of this, the demand for tools that help people in finding and analyzing all these resources are also growing in number. Text Classification, in particular, has been very useful in managing the information. Text Classification is the process of assigning natural language text to one or more categories based on the content. It has many important applications in the real world. For example, finding the sentiment of the reviews, posted by people on restaurants, movies and other such things are all applications of Text classification. In this project, focus has been laid on Sentiment Analysis, which identifies the opinions expressed in a piece of text. It involves categorizing opinions in text into categories like \u27positive\u27 or \u27negative\u27. Existing works in Sentiment Analysis focused on determining the polarity (Positive or negative) of a sentence. This comes under binary classification, which means classifying the given set of elements into two groups. The purpose of this research is to address a different approach for Sentiment Analysis called Multi Class Sentiment Classification. In this approach the sentences are classified under multiple sentiment classes like positive, negative, neutral and so on. Classifiers are built on the Predictive Model, that consists of multiple phases. Analysis of different sets of features on the data set, like stemmers, n-grams, tf-idf and so on, will be considered for classification of the data. Different classification models like Bayesian Classifier, Random Forest and SGD classifier are taken into consideration for classifying the data and their results are compared. Frameworks like Weka, Apache Mahout and Scikit are used for building the classifiers

Arabic sentence-level sentiment analysis

Sentiment analysis has recently become one of the growing areas of research related to text mining and natural language processing. The increasing availability of online resources and popularity of rich and fast resources for opinion sharing like news, online review sites and personal blogs, caused several parties such as customers, companies, and governments to start analyzing and exploring these opinions. The main task of sentiment classification is to classify a sentence (i.e. review, blog, comment, news, etc.) as holding an overall positive, negative or neutral sentiment. Most of the current studies related to this topic focus mainly on English texts with very limited resources available for other languages like Arabic, especially for the Egyptian dialect. In this research work, we would like to improve the performance measures of Egyptian dialect sentence-level sentiment analysis by proposing a hybrid approach which combines both the machine learning approach using support vector machines and the semantic orientation approach. Two methodologies were proposed, one for each approach, which were then joined, creating the hybrid proposed approach. The corpus used contains more than 20,000 Egyptian dialect tweets collected from Twitter, from which 4800 manually annotated tweets will be used (1600 positive tweets, 1600 negative tweets and 1600 neutral tweets). We performed several experiments to: 1) compare the results of each approach individually with regards to our case which is dealing with the Egyptian dialect before and after preprocessing; 2) compare the performance of merging both approaches together generating the hybrid approach against the performance of each approach separately; and 3) evaluate the effectiveness of considering negation on the performance of the hybrid approach. The results obtained show significant improvements in terms of the accuracy, precision, recall and F-measure, indicating that our proposed hybrid approach is effective in sentence-level sentiment classification. Also, the results are very promising which encourages continuing in this line of research

The Summarization of Arabic News Texts Using Probabilistic Topic Modeling for L2 Micro Learning Tasks

Report submitted as a result, in part, of participation in the Language Flagship Technology Innovation Center's Summer internship program in Summer 2019.The field of Natural Language Processing (NLP) combines computer science, linguistic theory, and mathematics. Natural Language Processing applications aim at equipping computers with human linguistic knowledge. Applications such as Information Retrieval, Machine Translation, spelling checkers, as well as text sum- marization, are intriguing fields that exploit the techniques of NLP. Text summariza- tion represents an important NLP task that simplifies various reading tasks. These NLP-based text summarization tasks can be utilized for the benefits of language acquisition.Language Flagship Technology Innovation Cente

Extraction of Keyphrases from Text: Evaluation of Four Algorithms

This report presents an empirical evaluation of four algorithms for automatically extracting keywords and keyphrases from documents. The four algorithms are compared using five different collections of documents. For each document, we have a target set of keyphrases, which were generated by hand. The target keyphrases were generated for human readers; they were not tailored for any of the four keyphrase extraction algorithms. Each of the algorithms was evaluated by the degree to which the algorithm’s keyphrases matched the manually generated keyphrases. The four algorithms were (1) the AutoSummarize feature in Microsoft’s Word 97, (2) an algorithm based on Eric Brill’s part-of-speech tagger, (3) the Summarize feature in Verity’s Search 97, and (4) NRC’s Extractor algorithm. For all five document collections, NRC’s Extractor yields the best match with the manually generated keyphrases

Exploring The Impact of Stemming on Text Topic-Based Classification Accuracy

Text classification attempts to assign written texts to specific group types that share the same linguistic features. One class of features that have been widely employed for a wide range of classification tasks is lexical features. This study explores the impact of stemming on text classification using lexical features. To explore, this study is based on a corpus of thirty texts written by six authors with topics that focus on politics, history, science, prose, sport, and food. These texts are stemmed using a light stemming algorithm. In order to classify these texts according to the topic by means of lexical features, linear hierarchical clustering and non-linear clustering (SOM) is carried out on the stemmed and unstemmed texts. Although both clustering methods are able to classify texts by topic with two models produce accurate and stable results, the results suggest that the impact of a light stemming on the accuracy of text classification by topic is ineffectual. The accuracy is neither increased nor decreased on the stemmed texts, whereby the stemming algorithm helped reducing the dimensionality of feature vector space model