MQVC: Measuring Quranic Verses Similarity and Sura Classification Using N-Gram

Extensive research efforts in the area of Information Retrieval were concentrated on developing retrieval systems related to Arabic language for the different natural language and information retrieval methodologies. However, little effort was conducted in those areas for knowledge extraction from the Holly Muslim book, the Quran. In this paper, we present an approach (MQVC) for retrieving the most similar verses in comparison with a user input verse as a query. To demonstrate the accuracy of our approach, we performed a set of experiments and compared the results with an evaluation from a Quran Specialist who manually identified all relevant chapters and verses to the targeted verse in our study. The MQVC approach was applied to 70 out of 114 Quran chapters. We picked 40 verses randomly and calculated the precision to evaluate the accuracy of our approach. We utilized N-gram to extend the work by performing experiment with machine learning algorithm (LibSVM classifier in Weka), to classify Quran chapters based on the most common scholars classification: Makki and Madani chapters

The Enhancement of Arabic Information Retrieval Using Arabic Text Summarization

The massive upload of text on the internet makes the text overhead one of the important challenges faces the Information Retrieval (IR) system. The purpose of this research is to maintain reasonable relevancy and increase the efficiency of the information retrieval system by creating a short and informative inverted index and by supporting the user query with a set of semantically related terms extracted automatically. To achieve this purpose, two new models for text mining are developed and implemented, the first one called Multi-Layer Similarity (MLS) model that uses the Latent Semantic Analysis (LSA) in the efficient framework. And the second is called the Noun Based Distinctive Verbs (NBDV) model that investigates the semantic meanings of the nouns by identifying the set of distinctive verbs that describe them. The Arabic Language has been chosen as the language of the case study, because one of the primary objectives of this research is to measure the effect of the MLS model and NBDV model on the relevancy of the Arabic IR (AIR) systems that use the Vector Space model, and to measure the accuracy of applying the MLS model on the recall and precision of the Arabic language text extraction systems. The initiating of this research requires holding a deep reading about what has been achieved in the field of Arabic information retrieval. In this regard, a quantitative relevancy survey to measure the enhancements achieved has been established. The survey reviewed the impact of statistical and morphological analysis of Arabic text on improving the AIR relevancy. The survey measured the contributions of Stemming, Indexing, Query Expansion, Automatic Text Summarization, Text Translation, Part of Speech Tagging, and Named Entity Recognition in enhancing the relevancy of AIR. Our survey emphasized the quantitative relevancy measurements provided in the surveyed publications. The survey showed that the researchers achieved significant achievements, especially in building accurate stemmers, with precision rates that convergent to 97%, and in measuring the impact of different indexing strategies. Query expansion and Text Translation showed a positive relevancy effect. However, other tasks such as Named Entity Recognition and Automatic Text Summarization still need more research to realize their impact on Arabic IR. The use of LSA in text mining demands large space and time requirements. In the first part of this research, a new text extraction model has been proposed, designed, implemented, and evaluated. The new method sets a framework on how to efficiently employ the statistical semantic analysis in the automatic text extraction. The method hires the centrality feature that estimates the similarity of the sentence with respect to every sentence found in the text. The new model omits the segments of text that have significant verbatim, statistical, and semantic resemblance with previously processed texts. The identification of text resemblance is based on a new multi-layer process that estimates the text-similarity at three statistical layers. It employes the Jaccard coefficient similarity and the Vector Space Model (VSM) in the first and second layers respectively and uses the Latent Semantic Analysis in the third layer. Due to high time complexity, the Multi-Layer model restricts the use of the LSA layer for the text segments that the Jaccard and VSM layers failed to estimate their similarities. ROUGE tool is used in the evaluation, and because ROUGE does not consider the extract’s size, it has been supplemented with a new evaluation strategy based on the ratio of sentences intersections between the automatic and the reference extracts and the condensation rate. The MLS model has been compared with the classical LSA that uses the traditional definition of the singular value decomposition and with the traditional Jaccard and VSM text extractions. The results of our comparison showed that the run of the LSA procedure in the MLS-based extraction reduced by 52%, and the original matrix dimensions dwindled by 65%. Also, the new method achieved remarkable accuracy results. We found that combining the centrality feature with the proposed multi-layer framework yields a significant solution regarding the efficiency and precision in the field of automatic text extraction. The automatic synonym extractor built in this research is based on statistical approaches. The traditional statistical approach in synonyms extraction is time-consuming, especially in real applications such as query expansion and text mining. It is necessary to develop a new model to improve the efficiency and accuracy during the extraction. The research presents the NBDV model in synonym extraction that replaces the traditional tf.idf weighting scheme with a new weighting scheme called the Orbit Weighing Scheme (OWS). The OWS weights the verbs based on their singularity to a group of nouns. The method was manipulated over the Arabic language because it has more varieties in constructing the verbal sentences than the other languages. The results of the new method were compared with traditional models in automatic synonyms extraction, such as the Skip-Gram and Continuous Bag of Words. The NBDV method obtained significant accuracy results (47% R and 51% P in the dictionary-based evaluation, and 57.5% precision using human experts’ assessment). It is found that on average, the synonyms extraction of a single noun requires the process of 186 verbs, and in 63% of the runs, the number of singular verbs was less than 200. It is concluded that the developed new method is efficient and processed the single run in linear time complexity (O(n)). After implementing the text extractors and the synonyms extractor, the VSM model was used to build the IR system. The inverted index was constructed from two sources of data, the original documents taken from various datasets of the Arabic language (and one from the English language for comparison purposes), and from the automatic summaries of the same documents that were generated from the automatic extractors developed in this research. A series of experiments were held to test the effectiveness of the extraction methods developed in this research on the relevancy of the IR system. The experiments examined three groups of queries, 60 Arabic queries with manual relevancy assessment, 100 Arabic queries with automatic relevancy assessment, and 60 English queries with automatic relevancy assessment. Also, the experiments were performed with and without synonyms expansions using the synonyms generated by the synonyms extractor developed in the research. The positive influence of the MLS text extraction was clear in the efficiency of the IR system without noticeable loss in the relevancy results. The intrinsic evaluation in our research showed that the bag of words models failed to reduce the text size, and this appears clearly in the large values of the condensation Rate (68%). Comparing with the previous publications that addressed the use of summaries as a source of the index, The relevancy assessment of our work was higher than their relevancy results. And, the relevancy results were obtained at 42% condensation rate, whereas, the relevancy results in the previous publication achieved at high values of condensation rate. Also, the MLS-based retrieval constructed an inverted index that is 58% smaller than the Main Corpus inverted index. The influence of the NBDV synonyms expansion on the IR relevancy had a slightly positive impact (only 1% improvement in both recall and precision), but no negative impact has been recorded in all relevancy measures

Towards effective cross-lingual search of user-generated internet speech

The very rapid growth in user-generated social spoken content on online platforms is creating new challenges for Spoken Content Retrieval (SCR) technologies. There are many potential choices for how to design a robust SCR framework for UGS content, but the current lack of detailed investigation means that there is a lack of understanding of the specifc challenges, and little or no guidance available to inform these choices. This thesis investigates the challenges of effective SCR for UGS content, and proposes novel SCR methods that are designed to cope with the challenges of UGS content. The work presented in this thesis can be divided into three areas of contribution as follows. The first contribution of this work is critiquing the issues and challenges that in influence the effectiveness of searching UGS content in both mono-lingual and cross-lingual settings. The second contribution is to develop an effective Query Expansion (QE) method for UGS. This research reports that, encountered in UGS content, the variation in the length, quality and structure of the relevant documents can harm the effectiveness of QE techniques across different queries. Seeking to address this issue, this work examines the utilisation of Query Performance Prediction (QPP) techniques for improving QE in UGS, and presents a novel framework specifically designed for predicting of the effectiveness of QE. Thirdly, this work extends the utilisation of QPP in UGS search to improve cross-lingual search for UGS by predicting the translation effectiveness. The thesis proposes novel methods to estimate the quality of translation for cross-lingual UGS search. An empirical evaluation that demonstrates the quality of the proposed method on alternative translation outputs extracted from several Machine Translation (MT) systems developed for this task. The research then shows how this framework can be integrated in cross-lingual UGS search to find relevant translations for improved retrieval performance

Latent Semantic Indexing (LSI) Based Distributed System and Search On Encrypted Data

Latent semantic indexing (LSI) was initially introduced to overcome the issues of synonymy and polysemy of the traditional vector space model (VSM). LSI, however, has challenges of its own, mainly scalability. Despite being introduced in 1990, there are few attempts that provide an efficient solution for LSI, most of the literature is focuses on LSI’s applications rather than improving the original algorithm. In this work we analyze the first framework to provide scalable implementation of LSI and report its performance on the distributed environment of RAAD. The possibility of adopting LSI in the field of searching over encrypted data is also investigated. The importance of that field is stemmed from the need for cloud computing as an effective computing paradigm that provides an affordable access to high computational power. Encryption is usually applied to prevent unauthorized access to the data (the host is assumed to be curious), however this limits accessibility to the data given that search over encryption is yet to catch with the latest techniques adopted by the Information Retrieval (IR) community. In this work we propose a system that uses LSI for indexing and free-query text for retrieving. The results show that the available LSI framework does scale on large datasets, however it had some limitations with respect to factors like dictionary size and memory limit. When replicating the exact settings of the baseline on RAAD, it performed relatively slower. This could be resulted by the fact that RAAD uses a distributed file system or because of network latency. The results also show that the proposed system for applying LSI on encrypted data retrieved documents in the same order as the baseline (unencrypted data)

Topic identification using filtering and rule generation algorithm for textual document

Information stored digitally in text documents are seldom arranged according to specific topics. The necessity to read whole documents is time-consuming and decreases the interest for searching information. Most existing topic identification methods depend on occurrence of terms in the text. However, not all frequent occurrence terms are relevant. The term extraction phase in topic identification method has resulted in extracted terms that might have similar meaning which is known as synonymy problem. Filtering and rule generation algorithms are introduced in this study to identify topic in textual documents. The proposed filtering algorithm (PFA) will extract the most relevant terms from text and solve synonym roblem amongst the extracted terms. The rule generation algorithm (TopId) is proposed to identify topic for each verse based on the extracted terms. The PFA will process and filter each sentence based on nouns and predefined keywords to produce suitable terms for the topic. Rules are then generated from the extracted terms using the rule-based classifier. An experimental design was performed on 224 English translated Quran verses which are related to female issues. Topics identified by both TopId and Rough Set technique were compared and later verified by experts. PFA has successfully extracted more relevant terms compared to other filtering techniques. TopId has identified topics that are closer to the topics from experts with an accuracy of 70%. The proposed algorithms were able to extract relevant terms without losing important terms and identify topic in the verse

Improving Retrieval Accuracy in Main Content Extraction from HTML Web Documents

The rapid growth of text based information on the World Wide Web and various applications making use of this data motivates the need for efficient and effective methods to identify and separate the “main content” from the additional content items, such as navigation menus, advertisements, design elements or legal disclaimers. Firstly, in this thesis, we study, develop, and evaluate R2L, DANA, DANAg, and AdDANAg, a family of novel algorithms for extracting the main content of web documents. The main concept behind R2L, which also provided the initial idea and motivation for the other three algorithms, is to use well particularities of Right-to-Left languages for obtaining the main content of web pages. As the English character set and the Right-to-Left character set are encoded in different intervals of the Unicode character set, we can efficiently distinguish the Right-to-Left characters from the English ones in an HTML file. This enables the R2L approach to recognize areas of the HTML file with a high density of Right-to-Left characters and a low density of characters from the English character set. Having recognized these areas, R2L can successfully separate only the Right-to-Left characters. The first extension of the R2L, DANA, improves effectiveness of the baseline algorithm by employing an HTML parser in a post processing phase of R2L for extracting the main content from areas with a high density of Right-to-Left characters. DANAg is the second extension of the R2L and generalizes the idea of R2L to render it language independent. AdDANAg, the third extension of R2L, integrates a new preprocessing step to normalize the hyperlink tags. The presented approaches are analyzed under the aspects of efficiency and effectiveness. We compare them to several established main content extraction algorithms and show that we extend the state-of-the-art in terms of both, efficiency and effectiveness. Secondly, automatically extracting the headline of web articles has many applications. We develop and evaluate a content-based and language-independent approach, TitleFinder, for unsupervised extraction of the headline of web articles. The proposed method achieves high performance in terms of effectiveness and efficiency and outperforms approaches operating on structural and visual features.Das rasante Wachstum von textbasierten Informationen im World Wide Web und die Vielfalt der Anwendungen, die diese Daten nutzen, macht es notwendig, effiziente und effektive Methoden zu entwickeln, die den Hauptinhalt identifizieren und von den zusätzlichen Inhaltsobjekten wie z.B. Navigations-Menüs, Anzeigen, Design-Elementen oder Haftungsausschlüssen trennen. Zunächst untersuchen, entwickeln und evaluieren wir in dieser Arbeit R2L, DANA, DANAg und AdDANAg, eine Familie von neuartigen Algorithmen zum Extrahieren des Inhalts von Web-Dokumenten. Das grundlegende Konzept hinter R2L, das auch zur Entwicklung der drei weiteren Algorithmen führte, nutzt die Besonderheiten der Rechts-nach-links-Sprachen aus, um den Hauptinhalt von Webseiten zu extrahieren. Da der lateinische Zeichensatz und die Rechts-nach-links-Zeichensätze durch verschiedene Abschnitte des Unicode-Zeichensatzes kodiert werden, lassen sich die Rechts-nach-links-Zeichen leicht von den lateinischen Zeichen in einer HTML-Datei unterscheiden. Das erlaubt dem R2L-Ansatz, Bereiche mit einer hohen Dichte von Rechts-nach-links-Zeichen und wenigen lateinischen Zeichen aus einer HTML-Datei zu erkennen. Aus diesen Bereichen kann dann R2L die Rechts-nach-links-Zeichen extrahieren. Die erste Erweiterung, DANA, verbessert die Wirksamkeit des Baseline-Algorithmus durch die Verwendung eines HTML-Parsers in der Nachbearbeitungsphase des R2L-Algorithmus, um den Inhalt aus Bereichen mit einer hohen Dichte von Rechts-nach-links-Zeichen zu extrahieren. DANAg erweitert den Ansatz des R2L-Algorithmus, so dass eine Sprachunabhängigkeit erreicht wird. Die dritte Erweiterung, AdDANAg, integriert eine neue Vorverarbeitungsschritte, um u.a. die Weblinks zu normalisieren. Die vorgestellten Ansätze werden in Bezug auf Effizienz und Effektivität analysiert. Im Vergleich mit mehreren etablierten Hauptinhalt-Extraktions-Algorithmen zeigen wir, dass sie in diesen Punkten überlegen sind. Darüber hinaus findet die Extraktion der Überschriften aus Web-Artikeln vielfältige Anwendungen. Hierzu entwickeln wir mit TitleFinder einen sich nur auf den Textinhalt beziehenden und sprachabhängigen Ansatz. Das vorgestellte Verfahren ist in Bezug auf Effektivität und Effizienz besser als bekannte Ansätze, die auf strukturellen und visuellen Eigenschaften der HTML-Datei beruhen

Plagiarism detection for Indonesian texts

As plagiarism becomes an increasing concern for Indonesian universities and research centers, the need of using automatic plagiarism checker is becoming more real. However, researches on Plagiarism Detection Systems (PDS) in Indonesian documents have not been well developed, since most of them deal with detecting duplicate or near-duplicate documents, have not addressed the problem of retrieving source documents, or show tendency to measure document similarity globally. Therefore, systems resulted from these researches are incapable of referring to exact locations of ``similar passage'' pairs. Besides, there has been no public and standard corpora available to evaluate PDS in Indonesian texts. To address the weaknesses of former researches, this thesis develops a plagiarism detection system which executes various methods of plagiarism detection stages in a workflow system. In retrieval stage, a novel document feature coined as phraseword is introduced and executed along with word unigram and character n-grams to address the problem of retrieving source documents, whose contents are copied partially or obfuscated in a suspicious document. The detection stage, which exploits a two-step paragraph-based comparison, is aimed to address the problems of detecting and locating source-obfuscated passage pairs. The seeds for matching source-obfuscated passage pairs are based on locally-weighted significant terms to capture paraphrased and summarized passages. In addition to this system, an evaluation corpus was created through simulation by human writers, and by algorithmic random generation. Using this corpus, the performance evaluation of the proposed methods was performed in three scenarios. On the first scenario which evaluated source retrieval performance, some methods using phraseword and token features were able to achieve the optimum recall rate 1. On the second scenario which evaluated detection performance, our system was compared to Alvi's algorithm and evaluated in 4 levels of measures: character, passage, document, and cases. The experiment results showed that methods resulted from using token as seeds have higher scores than Alvi's algorithm in all 4 levels of measures both in artificial and simulated plagiarism cases. In case detection, our systems outperform Alvi's algorithm in recognizing copied, shaked, and paraphrased passages. However, Alvi's recognition rate on summarized passage is insignificantly higher than our system. The same tendency of experiment results were demonstrated on the third experiment scenario, only the precision rates of Alvi's algorithm in character and paragraph levels are higher than our system. The higher Plagdet scores produced by some methods in our system than Alvi's scores show that this study has fulfilled its objective in implementing a competitive state-of-the-art algorithm for detecting plagiarism in Indonesian texts. Being run at our test document corpus, Alvi's highest scores of recall, precision, Plagdet, and detection rate on no-plagiarism cases correspond to its scores when it was tested on PAN'14 corpus. Thus, this study has contributed in creating a standard evaluation corpus for assessing PDS for Indonesian documents. Besides, this study contributes in a source retrieval algorithm which introduces phrasewords as document features, and a paragraph-based text alignment algorithm which relies on two different strategies. One of them is to apply local-word weighting used in text summarization field to select seeds for both discriminating paragraph pair candidates and matching process. The proposed detection algorithm results in almost no multiple detection. This contributes to the strength of this algorithm