An improved approach to English-Hindi based Cross Language Information Retrieval system

Katta, Eva; Arora, Anuja

doi:10.1109/ic3.2015.7346706

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…In this research work, initial query was extended by making additions ofrelevant wordsthat were extracted from the user's past information of one language. Article [21] gives importance for improvingthe searching and rankingelements of CLIR frameworkby making use of PSO (Particle Swarm Optimization) algorithm and Naïve Bayes. Retrieval) is a much-needed activity in a cross-lingual community as itcreates a mediumforaddressing the problem of language barriers.…”

Section: Literature Surveymentioning

confidence: 99%

Cloud Based Multi-Language Indexing Using Cross Lingual Information Retrieval Approaches

al.¹

2021

ITII

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The exponential growth of data sizes created by digital media (video/audio/images), physicalsimulations, scientific instruments and web authoring joins the new growth of interest in cloud computing. The options for distribution and parallelization of information in clouds make the retrieval and storage processes very complicated, especially when faced with real-time data management. The quantity of Web Users getting access to data over Internet is expanding step by step. An enormous measure of data on Internet is accessible in various languages which could be accessed by anyone whenever. The Information Retrieval (IR) manages finding valuable data from a huge assortment of unorganized, organized and semi-organized information. In the present situation, the variety of data and language boundaries are the difficult challenges for communication and social trade over the world. To tackle such obstructions, CLIR, the cross-language information retrieval frameworks, are these days in solid interest. The Query Expansion (Q.E.) is the way toward adding related and important terms to original inquiry to upgrade its indexing ability to improve the significance of recovered files in CLIR. In this exploration work, Q.E. has been investigated for a Hindi-English and Kannada-English CLIR in that Hindi and Kannada queries are utilized to look through English docs. After the interpretation of query, recovered outcomes are positioned making use of OkapiBM25 to organize the most important doc at the top for expanding the significance of recovered docs using QE. We proposed architecture for Hindi-English and Kannada-English CLIR making use of QE. to improve the importance of recovered reports. In the primary investigation, QE. is performed with and without OkapiBM25 ranking. The outcomes show that the pertinence of recovered archives is higher with OKapiBM25 as contrast with the one without positioning. The work docs plainly demonstrate that the presentation of Hindi-English and Kannada-English CLIR framework can be improved altogether with query development using fitting terms located at suitable place and the recovered Snippets can incredibly fill in as the continuous test collection.

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Section: Literature Surveymentioning

confidence: 99%

Cloud Based Multi-Language Indexing Using Cross Lingual Information Retrieval Approaches

al.¹

2021

ITII

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“…User contributed personal information has become important source of recommendation systems [24]. Even organizers/ Data owner companies which do not have social network wish to know user's personalized choice/ requirements through social networking sites.…”

Section: Background and Related Workmentioning

confidence: 99%

Cross-domain based Event Recommendation using Tensor Factorization

Arora

Taneja

Parashar

et al. 2016

Open Computer Science

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Abstract:Context in the form of meta-data has been accredited as an important component in cross-domain collaborative filtering (CDCF). In this research paper CDCF concept is used to exploit event information (context) from two UI matrices to allow the recommendation performance of one domain (Facebook-User-Event Matrix) to benefit from the information from another domain (Bookmyshow-Event-Tag Matrix). The model based collaborative filtering approach Tensor Factorization(TF) has been used to integrate Facebook provided User-Event context information with Bookmyshow Event-Tag context information to recommend events. In contrast to the standard collaborative tag recommendation, our CDCF approach uses one User-Event matrix of Facebook that takes another Bookmyshow Event-Tag matrix as additional informant. The proposed cross-domain based Event Recommendation approach is divided into three modules-i) data collection which extracts the unstructured dataset from the two domains Bookmyshow and social networking site Facebook using API's; ii) data mapping module which is basically used to integrate the common knowledge/ data that can be shared between considered different domains (Facebook & Bookmyshow). This module integrates and reduces the data into structured events' instances. As the dataset was collected from two different sites, an intersection of both was taken out. Therefore this module is carefully designed according to reliability of information that is common between two domains; iii) 3 order tensor factorization and Latent Dirichlet Allocation (LDA) used for most preferable recommendation by less pertinent result *Corresponding Author: Anuja Arora: CSE/IT Department, Jaypee Institute of Information Technology, Noida, India; Email: anuja.arora29@gmail.com Vaibhav Taneja: CSE/IT Department, Jaypee Institute of Information Technology, Noida, India; Email: vaibhavtaneja2@gmail.com Sonali Parashar: CSE/IT Department, Jaypee Institute of Information Technology, Noida, India; Email: sonali.parashar29@gmail.com Apurva Mishra: CSE/IT Department, Jaypee Institute of Information Technology, Noida, India; Email: apurvamishra95@gmail.com reduction. The proposed 3 order tensor factorization is designed for maximizing the mutual benefit from both the considered domains (organizer and user). Therefore providing three recommendations: For organizers: 1) system recommends places to conduct specific event according to maximum of attendees of a particular type of event at a specific location; 2) recommending target audience to organizer: those who are interested to attend event on the basis of past data for promotion purposes. For users: 3) recommending events to users of their interest on the basis of past record. Our result shows significant improvement in reduction of less relevant data and result effectiveness is measured through recall and precision. Reduction of less relevant recommendation is 64%, 72% and 63% for place recommendation to organizer, target audience recommendation to organizer and event recommendation to us...

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Product Diffusion Pattern Analysis Model Based on User’s Review of E-Commerce Application

Aggrawal

Arora

Jain

et al. 2017

Hybrid Intelligence for Social Networks

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An improved approach to English-Hindi based Cross Language Information Retrieval system

Cited by 9 publications

References 15 publications

Cloud Based Multi-Language Indexing Using Cross Lingual Information Retrieval Approaches

Cloud Based Multi-Language Indexing Using Cross Lingual Information Retrieval Approaches

Cross-domain based Event Recommendation using Tensor Factorization

Product Diffusion Pattern Analysis Model Based on User’s Review of E-Commerce Application

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