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<p>Natural Language Processing aims to facilitate computers to comprehend and refine human languages for some real-world as well as valuable objectives. While thousands of languages are spoken worldwide, language translation has been an utmost demanding and thought-provoking area of research. Researchers have been fruitful not only in representing these languages using machines but also in examining the elementary structure of these languages as reported by Niraj Aswani (in: Aligning sentences and words using English–Hindi bilingual parallel corpora). This research aims to develop a bilingual automatic AI-based text aligner using neural networks. The system inputs a text file from the user which comprises bilingual miscellaneous sentences. The bilingual text is processed based on the text sentences. The.csv file downloaded as the output consists of sentences placed adjacent to their equivalent sentences. In this, we will be working primarily with two languages English and Hindi. In this paper, we present a method for aligning English sentences with their corresponding Hindi translations at the sentence level, utilizing natural language processing and AI techniques. This approach aims to address a significant challenge in developing language models for various Indian languages, primarily due to the scarcity of aligned parallel bilingual data. In the results section, we will demonstrate the accuracy and efficiency of these models for English and Hindi, with potential applications for other Indian languages as well. The methodologies described above are typically based on either sentence length or word correspondences. Sentence-length-based approaches are generally faster and offer reasonable accuracy, while word correspondence methods tend to be more precise but are significantly slower, often relying on cognates or a bilingual lexicon. Our technique synthesizes and enhances these approaches, creating a system designed to align sentences in an English–Hindi corpus. This method achieves high accuracy at a relatively low computational cost, aiming to produce large-scale, high-quality aligned sentences between English and Hindi.</p>

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Auto language identifier and text aligner using neural network

  • 期刊论文

  • 发表日期：

    2025年8月14日

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  • Shashi Pal Singh,
  • Ritu Tiwari,
  • Sanjeev Sharma

摘要

Natural Language Processing aims to facilitate computers to comprehend and refine human languages for some real-world as well as valuable objectives. While thousands of languages are spoken worldwide, language translation has been an utmost demanding and thought-provoking area of research. Researchers have been fruitful not only in representing these languages using machines but also in examining the elementary structure of these languages as reported by Niraj Aswani (in: Aligning sentences and words using English–Hindi bilingual parallel corpora). This research aims to develop a bilingual automatic AI-based text aligner using neural networks. The system inputs a text file from the user which comprises bilingual miscellaneous sentences. The bilingual text is processed based on the text sentences. The.csv file downloaded as the output consists of sentences placed adjacent to their equivalent sentences. In this, we will be working primarily with two languages English and Hindi. In this paper, we present a method for aligning English sentences with their corresponding Hindi translations at the sentence level, utilizing natural language processing and AI techniques. This approach aims to address a significant challenge in developing language models for various Indian languages, primarily due to the scarcity of aligned parallel bilingual data. In the results section, we will demonstrate the accuracy and efficiency of these models for English and Hindi, with potential applications for other Indian languages as well. The methodologies described above are typically based on either sentence length or word correspondences. Sentence-length-based approaches are generally faster and offer reasonable accuracy, while word correspondence methods tend to be more precise but are significantly slower, often relying on cognates or a bilingual lexicon. Our technique synthesizes and enhances these approaches, creating a system designed to align sentences in an English–Hindi corpus. This method achieves high accuracy at a relatively low computational cost, aiming to produce large-scale, high-quality aligned sentences between English and Hindi.