Semantic speech query via stemmer for Quran documents 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.

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.

Sentence alignment using local and global information

International communication and multitude of information in several languages require information retrieval systems that can cross language borders. Cross-language information retrieval (CLIR) is defined as the retrieval of documents in another language than the language of the request or query in Anurag Seetha, et al (2004). The language of request is the source language and the language of documents is the target language. Among several approaches to CLIR, we opted for the method of translating queries for English-Hindi language pair based on a bilingual dictionary. This paper describes our first phase experiments with the CLIR system.

The study of cross-lingual Information Retrieval Systems (IRSs) and a deep analysis of system performances should provide guidelines, hints, and directions to drive the design and development of the next generation MultiLingual Information Access (MLIA) systems. In addition, effective tools for interpreting and comparing the experimental results should be made easily available to the research community. To this end, we propose a twofold methodology for the evaluation of Cross Language Information Retrieval (CLIR) systems: statistical analyses to provide MLIA researchers with quantitative and more sophisticated analysis techniques; and graphical tools to allow for a more qualitative comparison and an easier presentation of the results. We provide concrete examples about how the proposed methodology can be applied by studying the monolingual and bilingual tasks of the Cross-Language Evaluation Forum (CLEF) 2005 and 2006 campaigns.KeywordsInformation RetrievalMean Average PrecisionGraphical ToolBilingual ComparisonBilingual PerformanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper, we propose a cross-language information retrieval (CLIR) method based on estimating for domains of the query using hierarchic structures of Web directories. To get the most appropriate translation of the queries, we utilize the Web directories written in many different languages as multilingual corpus for disambiguating translation of the query and for estimating the domain of search results using hierarchic structures of Web directories. From experimental evaluations, we found that there is an advantage in retrieval accuracy using our proposal for disambiguating translation in CLIR system. We found that it is effective to restrict to target fields of the query using lower level merged categories in order to acquire suited translation of the query.

The query-based on the result is less relevant documents results. In this study, a query has been improved in order to retrieve more relevant documents across language boundaries, a mechanism for query translation with semantic which is applied on as semantic query (SQ). Therefore, this study is conducted with the purposes to investigate semantic 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 semantic approach is most important process in cross language information retrieval (CLIR). It also found that semantic approach contributes to better performance in retrieving more relevant and related Quran document results.

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..

We study the impact of translation resource scarcity on the performance of cross-language information retrieval (CLIR) systems. To do that, we develop a contrastive analysis framework that uses high-resource languages to simulate low-resource languages. In the framework, we focus on parallel translation corpora and aim to better understand the factors that impact CLIR performance. We argue that both low- and high-resource corpora are needed to develop that understanding. Hence, we take the approach of starting with a true low-resource language and systematically down-sampling a high-resource language to become an artificial low-resource language-the reverse perspective of existing research. We formalize the problem as the Resource Scarcity Simulation (RSS) problem. We model the problem with a family of set covering problems, formulate with integer linear programming, and prove that the problem is actually NP-hard. To this end, we provide two greedy algorithms with polynomial complexities. We compare and analyze our approach with alternate techniques using four high-resource languages (French, Italian, German, and Finnish) down-sampled to simulate two low-resource languages (Somali and Swahili). Our experimental results suggest that language families are important for the RSS problem. We simulate Somali with German, and Swahili with Finnish, achieving 98% and 97% on the similarity percentage in terms of CLIR performance, respectively.

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.

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.

The goal of many natural language processing platforms is to be able to someday correctly treat all languages. Each new language, especially one from a new language family, provokes some modification and design changes. Here we present the changes that we had to introduce into our platform designed for European languages in order to handle a Semitic language. Treatment of Arabic was successfully integrated into our cross language information retrieval system, which is visible online.

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.

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.