The role of Roman Urdu in multilingual information retrieval: A regional study

Safdar, Zanab; Bajwa, Ruqia Safdar; Hussain, Shafiq; Abdullah, Haslinda; Safdar, Kalsoom; Draz, Umar

doi:10.1016/j.acalib.2020.102258

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…The amazing accomplishments in sentiment analysis that neural nets have achieved are due to their capacity to handle sequences of various lengths. Between these neural nets, the long short-term memory units are noteworthy [27,36,21,24,29]. Recently deep learning technologies with multi-layer neural networks are widely used to improve the performance of prediction and classification [10,5].…”

Section: Objectives Of the Studymentioning

confidence: 99%

Aspect-based Text Classification for Sentimental Analysis using Attention mechanism with RU-BiLSTM

Yelisetti,

Nellore Geethanjali

2023

SCPE

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Sentiment analysis has gained increasing attention from an educational and social perspective with the huge expansion of user interactions due to the Web’s significant improvement. The connection between an opinion target’s polarity scores and other aspects of the content is defined by aspect-based sentiment analysis. Identifying aspects and determining their different polarities is quite complicated because they are frequently implicit. To overcome these difficulties, efficient hybrid methods are used in aspect-based text classification in sentiment analysis. The existing process evaluates the aspects of polarity by using a Convolutional neural network, and it does not work with Big data. In this work, aspect-based text classification and attention mechanisms are used to assist in filtering out irrelevant information and quickly locating the essential features in big data. Initially, the data is collected, and then the data is preprocessed by using Tokenization, Stop word removal, Stemming, and Lemmatization. After preprocessing, the features are vectorized and extracted using Bag-of-Words and TF-IDF. Then, the extracted features are given into word embeddings by GloVe and Word2vec. It uses Deep Recurrent based Bidirectional Long Short Term Memory (RUBiLSTM) for aspect-based sentiment analysis. The RU-Bi-LSTM method integrates aspect-based embeddings and an attention mechanism for text classification. The attention mechanism focuses on more crucial aspects and the bidirectional LSTM to maintain context in both ways. Finally, the binary and ternary classification outcomes are obtained using the final dense softmax output layer. The proposed RU-BiLSTM uses four reviews and two Twitter datasets. The results of the studies demonstrate the efficacy of the RU-BiLSTM model, which outperformed aspect-based classifications on lengthy reviews and short tweets in terms of evaluation.

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Section: Objectives Of the Studymentioning

confidence: 99%

Aspect-based Text Classification for Sentimental Analysis using Attention mechanism with RU-BiLSTM

Yelisetti,

Nellore Geethanjali

2023

SCPE

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“…It emphasizes the need for CLIR systems to bridge the language gap and allow effective retrieval of information in languages other than English (Suhas and Parag 2020).Cross Language Information Retrieval (CLIR) is a pivotal aspect of modern information systems that seeks to overcome the linguistic barriers inherent in our diverse global information landscape. As the world becomes increasingly interconnected, the volume of information generated and stored in various languages has surged (Hadni et al, 2014;Mahalakshmi et al, 2022;Safdar et al, 2020). This multilingual reality underscores the importance of CLIR, which is essentially the practice of enabling information retrieval systems to effectively search and retrieve data across languages different from the system's native language, typically English (Jacques Savoy2004, Monika and Sudha 2015, Suhas et.…”

Section: Cross Language Information Retrieval (Clir) and The Importan...mentioning

confidence: 99%

MarQO: A query optimizer in multilingual environment for information retrieval in Marathi language

Suhas D. Pachpande,

Parag U. Bhalchandra

2023

Int. J. Sci. Res. Arch.

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Information retrieval is a crucial component of modern information systems. A significant portion of the vast amount of information stored worldwide is in local languages. While most information retrieval systems are designed primarily for English, there is a growing need for these systems to work with data in languages other than English. Cross Language Information Retrieval (CLIR) systems play a pivotal role in enabling information retrieval across multiple languages. However, these systems often face challenges due to ambiguities in query translation, impacting retrieval accuracy. This paper introduces "MarQO," a query optimizer designed to address these challenges in the context of Marathi language. MarQO employs a multi-stage approach, including lexical processing, extraction of multi-word terms, synonym addition, phrasal translations, utilization of word co-occurrence statistics, and more. By disambiguating query keyword translations, MarQO significantly improves the accuracy of translations, thereby leading to more relevant document retrieval results.

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“…Information Retrieval (IR) stands at the core of our digital age, serving as the backbone of search engines, digital libraries, and countless applications that help users access and navigate the vast ocean of digital information (Manning et al, 2008). IR encompasses the systematic process of retrieving pertinent information from extensive collections, often referred to as document corpora, based on user queries or search terms (Safdar et al, 2020). This retrieval process plays a pivotal role in ensuring that users can access relevant, reliable, and timely information.…”

Section: Introductionmentioning

confidence: 99%

Cross Language Information Retrieval based on Automatic Query Translation for Marathi Documents

Pachpande,

Bhalchandra

2023

IJRASET

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The current research article explores the realm of Cross Language Information Retrieval (CLIR) and its significance in the digital age. It addresses the challenges faced in CLIR, including lexical and semantic disparities, the scarcity of parallel corpora, cultural nuances, and more. The article discusses innovative solutions encompassing Machine Translation, Query Expansion, Cross-Lingual Word Embeddings, and Multilingual Information Retrieval Models to enhance CLIR's effectiveness. Furthermore, it sheds light on Information Retrieval Models, such as the Boolean Model, Vector Space Model (VSM), and Probabilistic Models, explaining their principles and applications. The study also presents experimental results highlighting the limitations of monolingual IR models and the effectiveness of crosslingual techniques, such as translation and query expansion, in improving CLIR, making it a valuable tool for accessing information across languages.

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The role of Roman Urdu in multilingual information retrieval: A regional study

Cited by 10 publications

References 18 publications

Aspect-based Text Classification for Sentimental Analysis using Attention mechanism with RU-BiLSTM

Aspect-based Text Classification for Sentimental Analysis using Attention mechanism with RU-BiLSTM

MarQO: A query optimizer in multilingual environment for information retrieval in Marathi language

Cross Language Information Retrieval based on Automatic Query Translation for Marathi Documents

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