Arabic Information Retrieval: A Relevancy Assessment Survey

The paper presents a research in Arabic Information Retrieval (IR). It surveys the impact of statistical and morphological analysis of Arabic text in improving Arabic IR relevancy. We investigated the contributions of Stemming, Indexing, Query Expansion, Text Summarization (TS), Text Translation, and Named Entity Recognition (NER) in enhancing the relevancy of Arabic IR. Our survey emphasizing on the quantitative relevancy measurements provided in the surveyed publications. The paper shows that the researchers achieved significant enhancements especially in building accurate stemmers, with accuracy reaches 97%, and in measuring the impact of different indexing strategies. Query expansion and Text Translation showed positive relevancy effect. However, other tasks such as NER and TS still need more research to realize their impact on Arabic IR

Utilisation of metadata fields and query expansion in cross-lingual search of user-generated Internet video

Recent years have seen signicant eorts in the area of Cross Language Information Retrieval (CLIR) for text retrieval. This work initially focused on formally published content, but more recently research has begun to concentrate on CLIR for informal social media content. However, despite the current expansion in online multimedia archives, there has been little work on CLIR for this content. While there has been some limited work on Cross-Language Video Retrieval (CLVR) for professional videos, such as documentaries or TV news broadcasts, there has to date, been no signicant investigation of CLVR for the rapidly growing archives of informal user generated (UGC) content. Key differences between such UGC and professionally produced content are the nature and structure of the textual UGC metadata associated with it, as well as the form and quality of the content itself. In this setting, retrieval eectiveness may not only suer from translation errors common to all CLIR tasks, but also recognition errors associated with the automatic speech recognition (ASR) systems used to transcribe the spoken content of the video and with the informality and inconsistency of the associated user-created metadata for each video. This work proposes and evaluates techniques to improve CLIR effectiveness of such noisy UGC content. Our experimental investigation shows that dierent sources of evidence, e.g. the content from dierent elds of the structured metadata, significantly affect CLIR effectiveness. Results from our experiments also show that each metadata eld has a varying robustness to query expansion (QE) and hence can have a negative impact on the CLIR eectiveness. Our work proposes a novel adaptive QE technique that predicts the most reliable source for expansion and shows how this technique can be effective for improving CLIR effectiveness for UGC content

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

بناء أداة تفاعلية متعددة اللغات لاسترجاع المعلومات

The growing requirement on the Internet have made users access to the information expressed in a language other than their own , which led to Cross lingual information retrieval (CLIR) .CLIR is established as a major topic in Information Retrieval (IR). One approach to CLIR uses different methods of translation to translate queries to documents and indexes in other languages. As queries submitted to search engines suffer lack of untranslatable query keys (i.e., words that the dictionary is missing) and translation ambiguity, which means difficulty in choosing between alternatives of translation. Our approach in this thesis is to build and develop the software tool (MORTAJA-IR-TOOL) , a new tool for retrieving information using programming JAVA language with JDK 1.6. This tool has many features, which is develop multiple systematic languages system to be use as a basis for translation when using CLIR, as well as the process of stemming the words entered in the query process as a stage preceding the translation process. The evaluation of the proposed methodology translator of the query comparing it with the basic translation that uses readable dictionary automatically the percentage of improvement is 8.96%. The evaluation of the impact of the process of stemming the words entered in the query on the quality of the output process in the retrieval of matched data in other process the rate of improvement is 4.14%. Finally the rated output of the merger between the use of stemming methodology proposed and translation process (MORTAJA-IR-TOOL) which concluded that the proportion of advanced in the process of improvement in data rate of retrieval is 15.86%. Keywords: Cross lingual information retrieval, CLIR, Information Retrieval, IR, Translation, stemming.الاحتياجات المتنامية على شبكة الإنترنت جعلت المستخدمين لهم حق الوصول إلى المعلومات بلغة غير لغتهم الاصلية، مما يقودنا الى مصطلح عبور اللغات لاسترجاع المعلومات (CLIR). CLIR أنشئت كموضوع رئيسي في "استرجاع المعلومات" (IR). نهج واحد ل CLIR يستخدم أساليب مختلفة للترجمة ومنها لترجمة الاستعلامات وترجمة الوثائق والفهارس في لغات أخرى. الاستفسارات والاستعلامات المقدمة لمحركات البحث تعاني من عدم وجود ترجمه لمفاتيح الاستعلام (أي أن العبارة مفقودة من القاموس) وايضا تعاني من غموض الترجمة، مما يعني صعوبة في الاختيار بين بدائل الترجمة. في نهجنا في هذه الاطروحة تم بناء وتطوير الأداة البرمجية (MORTAJA-IR-TOOL) أداة جديدة لاسترجاع المعلومات باستخدام لغة البرمجة JAVA مع JDK 1.6، وتمتلك هذه الأداة العديد من الميزات، حيث تم تطوير منظومة منهجية متعددة اللغات لاستخدامها كأساس للترجمة عند استخدام CLIR، وكذلك عملية تجذير للكلمات المدخلة في عملية الاستعلام كمرحلة تسبق عملية الترجمة. وتم تقييم الترجمة المنهجية المقترحة للاستعلام ومقارنتها مع الترجمة الأساسية التي تستخدم قاموس مقروء اليا كأساس للترجمة في تجربة تركز على المستخدم وكانت نسبة التحسين 8.96% , وكذلك يتم تقييم مدى تأثير عملية تجذير الكلمات المدخلة في عملية الاستعلام على جودة المخرجات في عملية استرجاع البيانات المتطابقة باللغة الاخرى وكانت نسبة التحسين 4.14% , وفي النهاية تم تقييم ناتج عملية الدمج بين استخدام التجذير والترجمة المنهجية المقترحة (MORTAJA-IR-TOOL) والتي خلصت الى نسبة متقدمة في عملية التحسين في نسبة البيانات المرجعة وكانت 15.86%

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

Toward higher effectiveness for recall-oriented information retrieval: A patent retrieval case study

Research in information retrieval (IR) has largely been directed towards tasks requiring high precision. Recently, other IR applications which can be described as recall-oriented IR tasks have received increased attention in the IR research domain. Prominent among these IR applications are patent search and legal search, where users are typically ready to check hundreds or possibly thousands of documents in order to find any possible relevant document. The main concerns in this kind of application are very different from those in standard precision-oriented IR tasks, where users tend to be focused on finding an answer to their information need that can typically be addressed by one or two relevant documents. For precision-oriented tasks, mean average precision continues to be used as the primary evaluation metric for almost all IR applications. For recall-oriented IR applications the nature of the search task, including objectives, users, queries, and document collections, is different from that of standard precision-oriented search tasks. In this research study, two dimensions in IR are explored for the recall-oriented patent search task. The study includes IR system evaluation and multilingual IR for patent search. In each of these dimensions, current IR techniques are studied and novel techniques developed especially for this kind of recall-oriented IR application are proposed and investigated experimentally in the context of patent retrieval. The techniques developed in this thesis provide a significant contribution toward evaluating the effectiveness of recall-oriented IR in general and particularly patent search, and improving the efficiency of multilingual search for this kind of task

Multilingual Information Access: Practices and Perceptions of Bi/multilingual Academic Users

The research reported in this dissertation explored linguistic determinants in online information searching, and examined to what extent bi/multilingual academic users utilize Multilingual Information Access (MLIA) tools and what impact these have on their information searching behavior. The aim of the study was three-pronged: to provide tangible data that can support recommendations for the effective user-centered design of Multilingual Information Retrieval (MLIR) systems; to provide a user-centered evaluation of existing MLIA tools, and to offer the basis of a framework for Library & Information Science (LIS) professionals in teaching information literacy and library skills for bi/multilingual academic users. In the first phase of the study, 250 bi/multilingual students participated in a web survey that investigated their language choices while searching for information on the internet and electronic databases. 31 of these participants took part in the second phase which involved a controlled lab-based user experiment and post experiment questionnaire that investigated their use of MLIA tools on Google and WorldCat and their opinions of these tools. In the third phase, 19 students participated in focus groups discussions and 6 librarians were interviewed to find out their perspectives on multilingual information literacy. Results showed that though machine translation has alleviated some of the linguistic related challenges in online information searching, language barriers do still exist for some users especially at the query formulation stage. Captures from the experiment revealed great diversity in the way MLIA tools were utilized while the focus group discussions and interviews revealed a general lack of awareness by both librarians and students of the tools that could help enhance and promote multilingual information literacy. The study highlights the roles of both IR system designers as well as LIS professionals in enhancing and promoting multilingual information access and literacy: User- centered design, user-modeling were found to be key aspects in the development of more effective multilingual information retrieval (MLIR) systems. The study also highlights the distinction between being multilingually information literate and being multilingual information literate. Suitable models for instruction for bi/multilingual academic users point towards Specialized Information Literacy Instruction (SILI) and Personalized Information Literacy Instruction (PILI)

Effective retrieval techniques for Arabic text

Arabic is a major international language, spoken in more than 23 countries, and the lingua franca of the Islamic world. The number of Arabic-speaking Internet users has grown over nine-fold in the Middle East between the year 2000 and 2007, yet research in Arabic Information Retrieval (AIR) has not advanced as in other languages such as English. In this thesis, we explore techniques that improve the performance of AIR systems. Stemming is considered one of the most important factors to improve retrieval effectiveness of AIR systems. Most current stemmers remove affixes without checking whether the removed letters are actually affixes. We propose lexicon-based improvements to light stemming that distinguish core letters from proper Arabic affixes. We devise rules to stem most affixes and show their effects on retrieval effectiveness. Using the TREC 2001 test collection, we show that applying relevance feedback with our rules produces significantly better results than light stemming. Techniques for Arabic information retrieval have been studied in depth on clean collections of newswire dispatches. However, the effectiveness of such techniques is not known on other noisy collections in which text is generated using automatic speech recognition (ASR) systems and queries are generated using machine translations (MT). Using noisy collections, we show that normalisation, stopping and light stemming improve results as in normal text collections but that n-grams and root stemming decrease performance. Most recent AIR research has been undertaken using collections that are far smaller than the collections used for English text retrieval; consequently, the significance of some published results is debatable. Using the LDC Arabic GigaWord collection that contains more than 1 500 000 documents, we create a test collection of~90 topics with their relevance judgements. Using this test collection, we show empirically that for a large collection, root stemming is not competitive. Of the approaches we have studied, lexicon-based stemming approaches perform better than light stemming approaches alone. Arabic text commonly includes foreign words transliterated into Arabic characters. Several transliterated forms may be in common use for a single foreign word, but users rarely use more than one variant during search tasks. We test the effectiveness of lexicons, Arabic patterns, and n-grams in distinguishing foreign words from native Arabic words. We introduce rules that help filter foreign words and improve the n-gram approach used in language identification. Our combined n-grams and lexicon approach successfully identifies 80% of all foreign words with a precision of 93%. To find variants of a specific foreign word, we apply phonetic and string similarity techniques and introduce novel algorithms to normalise them in Arabic text. We modify phonetic techniques used for English to suit the Arabic language, and compare several techniques to determine their effectiveness in finding foreign word variants. We show that our algorithms significantly improve recall. We also show that expanding queries using variants identified by our Soutex4 phonetic algorithm results in a significant improvement in precision and recall. Together, the approaches described in this thesis represent an important step towards realising highly effective retrieval of Arabic text