Semantic query for Quran documents results

Speech recognition has provided limited converted speech into text as query for cross language information retrieval (CLIR). Therefore, in this study, speech recognition to be integrated with cross language information retrieval system to investigate the Quran system results performance using semantic and stemmers as stemming semantic spoken query (SSSQ). The query however is based on the speech to be input and converted into text. Therefore, this study is conducted with the purposes to investigate the integration speech, semantic and stemmers approach against the queries and vice versa. Furthermore, it is also conducted to investigate the performance query based on total retrieve and relevant. The retrieval however, included the irrelevant documents because of the translation polysemy. Results from the experiments suggest that SSSQ is most important process in CLIR. It also found that semantic speech approach with stemmers contributes to better approach in using speech instead of keyboard to investigate its performance for Quran document results.

Cross language information retrieval (CLIR) is a retrieval process in which the user fires queries in one language to retrieve information from another (different) language. The diversity of information and language barriers are the serious issues for communication and cultural exchange across the world. To solve such barriers, Cross language information retrieval system, are nowadays in strong demand. CLIR is a subset of Information Retrieval (IR) system. Information Retrieval deals with finding useful information from a large collection of unstructured, structured and semi-structured data to a user query where the query is a set of keywords. Information Retrieval can be classified into different classes such as Monolingual information retrieval, Bi-Lingual Information Retrieval, Multilingual information retrieval and Cross language information retrieval. This paper focuses on the various IR variants and techniques used in CLIR system. Further, based on available literature, a number of challenges and issues in CLIR have been identified and discussed. It gives an overview of the advantages, limitations, tools available in CLIR research. It also describes new application areas of CLIR such as medical, multimedia, question answering system etc. The need for exploring and building more specialized information system that enable speakers of an Odia language to discover valuable information beyond linguistic and cultural barriers. This study is aimed at building an experimental CLIR system between one of the under-resourced language (i.e. Odia) and one of the most commonly used online language (i.e. English) in future.

In Cross-Language Information Retrieval (CLIR) process, the translation effects have a direct impact o n the accuracy of follow-up retrieval results. In diction ary-based approach, we are dealing with the words t hat have more than one meaning which can decrease the retrieval performance if the query translation retur n an incorrect translations. These issues need to be ove rcome using efficient technique. In this study we p roposed a Cross-Language Information Retrieval (CLIR) method based on domain ontology using Quran concepts for disambiguating translation of the query and to improve the dictionary-based query translation. For experimentation, we use Quran ontology written in E nglish and Malay languages as a bilingual parallelcorpora and Quran concepts as a resource for cross- language query translation along with dictionary-ba sed translation. For evaluation, we measure the perform ance of three IR systems. IR 1 is natural language query IR, IR 2 is natural language query CLIR based on dictionary (as a Baseline) and IR 3 is the retrieval of this research proposed method using Mean Average Precision (MAP) and average precision at 11 points of recall. The experimental result shows that our prop osed method brings significant improvement in retri eval accuracy for English document collections, but defi cient for Malay document collections. The proposed CLIR method can obtain query expansion effect and improve retrieval performance in certain language.

The development of digital and online information repositories is creating many opportunities and also new challenges in information retrieval. The availability of online documents in many different languages makes it possible for users around the world to directly access previously unimagined sources of information. However in conventional information retrieval systems the user must enter a search query in the language of the documents in order to retrieve it. This requires that users can express their queries in those languages in which the information is available and can understand the documents returned by the retrieval process. This restriction clearly limits the amount and type of information that an individual user really has access to. Cross Lingual Information Access is concerned with technologies that let users express their query in their native language, and irrespective of the language in which the information is available, present their information in the user-preferred language or set of languages, in a manner that satisfies the user's information needs. The additional processing may take the form of machine translation of snippets, summarization and subsequent translation of summaries and/or information extraction. In recent times, research in Cross Lingual Information Access has been vigorously pursued through several international fora, such as, the Cross-Language Evaluation Forum (CLEF), NTCIR Asian Language Retrieval, Question-answering Workshop and such other fora. A workshop geared towards cross language information retrieval in Indian languages (FIRE) was organized in December 2008. In addition to CLIR, significant results have been obtained in multilingual summarization workshops and cross-language named entity extraction challenges by the ACL (Association for Computational Linguistics) and the Geographic Information retrieval (GeoCLEF) track of CLEF. The previous two issues of this workshop were held in January 2007, during IJCAI 2007 in Hyderabad, India (http://search.iiit.ac.in/CLIA2007/) and subsequently during IJCNLP 2008 in Hyderabad, India (http://search.iiit.ac.in/CLIA2008/). Both the previous workshops attracted an encouraging number of submissions, and a large number of registered participants. This third international workshop on Cross Lingual Information Access aims to bring together various trends in multi-source, cross and multilingual information retrieval and access, and provide a venue for researchers and practitioners from academia, government, and industry to interact and share a broad spectrum of ideas, views and applications. The present workshop includes an invited keynote talk, presentations of technical papers selected after peer review followed by a panel discussion.

Cross language information retrieval (CLIR) deals with the use of queries in one language to access documents in another. A popular CLIR approach is to translate the query into the language of the documents being retrieved. The simplest yet most effective method for query translation is bilingual dictionary approach. However, direct translation using bilingual dictionary prune two problems: proper names and compound words translations. In this study, a series of experiments were conducted to test and evaluate the effectiveness of the Malay-English CLIR using proper names and compound words translation. We believe by using concept-based indexing and translations, makes proper names and compound words translation possible. The best retrieval performance was obtained from the combination of query translation approach-select all translations listed in the dictionary, the alternative weighting scheme and proper names identification and translation.

Researchers in multilingual information retrieval and natural language processing are making progress on algorithms for guessing what a text is about and for translating queries between languages; some day we may have reliable programs for recognizing musical melodies or the content of images. In contrast, researchers in metadata focus more on seeking consensus on the meanings of categories for describing resources; on finding ways to allow simple schemas to interoperate with complex ones; and on designing frameworks for managing the messy equivalencies between metadata models in different fields and languages. Do the two perspectives form a continuum? What problems do they solve best? For resource discovery, what is the best balance between human and machine?.

Paraphrase identification is a crucial task in natural language understanding, especially in cross-language information retrieval. Nowadays, Multi-Task Deep Neural Network (MT-DNN) has become a state-of-the-art method that brings outstanding results in paraphrase identification [1]. In this paper, our proposed method based on MT-DNN [2] to detect similarities between English and Vietnamese sentences, is proposed. We changed the shared layers of the original MT-DNN from original the BERT [3] to other pre-trained multi-language models such as M-BERT [3] or XLM-R [4] so that our model could work on cross-language (in our case, English and Vietnamese) information retrieval. We also added some tasks as improvements to gain better results. As a result, we gained 2.3% and 2.5% increase in evaluated accuracy and F1. The proposed method was also implemented on other language pairs such as English – German and English – French. With those implementations, we got a 1.0%/0.7% improvement for English – German and a 0.7%/0.5% increase for English – French.

Given a user question, the goal of a Question Answering (QA) system is to retrieve answers rather than full documents or even best-matching passages, as most Information Retrieval systems currently do. In this paper, we present BRUJA, a QA system for the management of multilingual collections. BRUJ rkstions (English, Spanish and French). The BRUJA architecture is not formed with three monolingual QA systems but instead uses English as Interlingua to make usual QA tasks such as question classifications and answer extractions. In addition, BRUJA uses Cross Language Information Retrieval (CLIR) techniques to retrieve relevant documents from a multilingual collection. On the one hand, we have more documents to find answers from but on the other hand, we are introducing noise into the system because of translations to the Interlingua (English) and the CLIR module. The question is whether the difficulty of managing three languages is worth it or whether a monolingual QA system delivers better results. We report on in-depth experimentation and demonstrate that our multilingual QA system gets better results than its monolingual counterpart whenever it uses good translation resources and, especially, CLIR techniques that are state-of-the-art.

Proper nouns are usually primary keys in a query. Their correct translation might be necessary to maintain a good retrieval performance in a cross language information retrieval (CLIR) system. However, dictionaries only include the most commonly used proper nouns, like major countries and capitals. As they are spelling variants of each other in most languages, using an approximate string matching technique against the target database index is the common approach taken to find the target language correspondents of the original query key. N-gram technique proved to be the most effective among other approximate string matching techniques. As we are dealing with an English-Arabic CLIR system which involves two languages of different alphabets, we decided to combine transliteration with the n-gram technique to generate the different spelling variants of out of vocabulary (OOV) words. We call this technique: Transliteration Ngram (TNG). One issue that arises with the Arabic language is that words that are spelled similarly can have different meanings depending on the context of the sentence. This is particularly true for proper names, which usually have a meaning if used as a verb or adjective. To further enhance our transliteration approach, we chose to use part of speech (POS) disambiguation to reduce the number of unrelated words from the set transliterations obtained using TNG.

Machine Transliteration is to transcribe a word written in a script with approximate phonetic equivalence in another language. It is useful for machine translation, cross-lingual information retrieval, multilingual text and speech processing. Punjabi Machine Transliteration (PMT) is a special case of machine transliteration and is a process of converting a word from Shahmukhi (based on Arabic script) to Gurmukhi (derivation of Landa, Shardha and Takri, old scripts of Indian subcontinent), two scripts of Punjabi, irrespective of the type of word.The Punjabi Machine Transliteration System uses transliteration rules (character mappings and dependency rules) for transliteration of Shahmukhi words into Gurmukhi. The PMT system can transliterate every word written in Shahmukhi.

In this paper we focus on the design and implementation of low cost, cross language and cross platform Information Retrieval and Documentation tools capable for the collection, organization and administration of unstructured and semi-structured information imported from various sources. A modular Computer Assisted Information Resources Navigation (CAIRN) software architecture is proposed and the requirements of each module are presented. A discussion of the implementation is based on the experimentation with a prototype of such a software tool. The technologies that are incorporated into the modern operating systems and the opportunities that they offer for implementing the modules of the CAIRN architecture are also examined and evaluated. Some of these technologies are common / independent from the operating systems, while some others are distinctive. In this latter case we face barriers (restrictions) for a straightforward implementation of the CAIRN software systems to the whole range of desktop operating systems (e.g. Windows, Mac OS, Linux, Solaris). Some alternative technologies are presented to avoid this serious constraint. The evaluation of the implementation effort is also discussed and eventually some conclusions and future plans for further improvement of the CAIRN architecture are given.

Named Entity Recognition is a subtask in Information Extraction. In Named Entity Recognition (NER) we try to identify each of the words provided into some categories predefined by us. These categories or classes can be organization, name, time, place etc. Named Entity Recognition is a part of Natural Language Processing which is aims at making human and computer interactions more meaningful and efficient. NLP is nowadays being effectively being used in the area of automatic text summarization, cross language information retrieval, speech recognition and query named entity recognition. This paper focuses on Named entity recognition, how it is performed and a how apply NER to web search queries so that correct and more user personalized results are displayed by the web search engines..

Recent availability of commercial online machine translation (MT) systems makes it possible for layman Web users to utilize the MT capability for cross-language information retrieval (CLIR). To study the effectiveness of using MT for query translation, we conducted a set of experiments using Google Translate, an online MT system provided by Google, for translating queries in CLIR. The experiments show that MT is an excellent tool for the query translation task, and with the help of relevance feedback, it can achieve significant improvement over the monolingual baseline. The MT based query translation not only works for long queries, but is also effective for the short Web queries.

Cross lingual Information Retrieval (CLIR) refers to the information retrieval activities in which the query and/or documents may appear in different languages. Dictionary-based query translation has been a common method in CLIR systems. In these methods we face with the problem of translation ambiguity in which a single word in one language has more than one translation in the other language. In this paper we propose a hybrid approach to retrieve English documents relevant to Persian queries. In this approach we exploit a combination of phrase reorganization, pattern based phrase translation and query expansion before and after translation to improve the dictionary-based query translation. We also propose an improved probabilistic algorithm to choose the best translation of words and phrases. Finally, the documents will be ranked according to statistical language model with some translation steps. Our experimental results show that each of the mentioned methods can bring significant improvement over simple dictionary approaches.

In this paper we investigate some of the most important variants of information retrieval. They are Cross-lingual Information Retrieval (CLIR) and Multilingual Information Retrieval (MLIR). CLIR deals with submitting queries in one language and retrieving documents in some other language other than query language. MLIR deals with submitting questions in one or more languages and retrieving documents in one or more diverse languages. With increasing globalized economy, the knack to find information in other languages is becoming a necessity. In short the paper tries to encapsulate the overall review of the research works in Indian and Foreign languages.