Marathi Language Processing Challenges and Advancements

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मराठी एक प्रमुख भाषा आहे ज्या ____________ वापरतात


मराठी भाषेतील संवादाचा मुख्य साधन कोणता आहे? ____________


मराठी सामग्रीच्या भावना विश्लेषणासाठी कोणत्या प्रगत भाषा प्रसंस्करण तंत्रज्ञानांचा वापर केला जातो? ____________


मराठी भाषा प्रसंस्करणाच्या क्षेत्रात किती ______ आहेत?


मराठी सामग्रीच्या भावना विश्लेषणात शोधकांची एक अद्वितीय प्रयत्न आहे ____________


मराठी भाषेत संवेदना-संबंधित विषयांचे ओळखणं किती ______ आहे?


महाइमोसेन अप्रत्याशित काम करतो का? ____________


उच्च-गुणवत्ताची मायक्रोब्लॉग-विशिष्ट संवेदना शब्दकोशांची निर्मिती ______ अद्याप लाभदायक ठरू शकते.


मालिकेतील पाठविलेल्या भावना विश्लेषणात कसे मदत करते? ____________

मशीन लर्निंग

भारतीय उपखंडाच्या भाषांच्या जगात हिसाबात पडलेली भाषांपेक्षा, संगणकीय भाषाशास्त्राचे उपकरण आणि विधान ______ करण्यास वेळ आहे.


मराठी भाषेच्या क्षेत्रात हिंदी आणि मराठीत SQuAD 2.0 डेटासेटचे भाषांतर करण्याच्या प्रयत्नांमध्ये किती ______ उभे आहेत?


मराठी भाषेच्या क्षेत्रात प्रगतीशील विकासांच्या काही आशावादी विकासांपासून बरेच ______ आहेत.


Study Notes


Marathi is one of the major languages spoken in India, particularly in the state of Maharashtra, where roughly half of the population uses it as their primary means of communication. As a result of global digital culture and technological advancements, researchers are increasingly interested in exploring ways to apply advanced language processing techniques to understand and analyze Marathi content. This includes sentiment analysis, which involves determining the emotional tone or attitude expressed within texts, and question answering, which aims to accurately interpret questions posed in a given language. Both areas have seen significant progress in recent years, despite challenges related to data scarcity and the complexity of Marathi as a language.

Emotion-Aware Multimodal Marathi Sentiment Analysis

One notable development is an effort called MahaEmoSen, which proposes an emotion-aware multimodal Marathi sentiment analysis system. This system goes beyond traditional sentence-level analysis to consider emotions embedded in tweets and combines textual and visual modalities from social media posts to perform sentiment classification. By leveraging machine learning techniques, including word-level attention mechanisms and data augmentation strategies, the study demonstrates improved performance compared to earlier sentiment analysis methods when dealing with resource-constrained situations.

Question Answering for Hindi and Marathi

To address the shortage of suitable question answering datasets for these under-resourced languages, there has been an initiative to translate the SQuAD 2.0 dataset into Hindi and Marathi. This translated dataset serves as a foundation for developing effective question answering systems in these languages, although these efforts still need to overcome issues surrounding data dearth and the peculiarities inherent to regional languages.

Challenges and Future Directions

Despite some promising developments, the field of Marathi language processing faces numerous hurdles. For instance, the detection of sentiment-related topics is crucial yet largely unexplored. Additionally, the construction of high-quality microblog-specific sentiment lexicons would greatly contribute to sentiment analysis systems designed specifically for Marathi. Moreover, the vast majority of computational linguistics tools and techniques have historically prioritized western languages over those of the Indian subcontinent, leaving room for improvement in the realm of Marathi language analysis.

Explore the advancements and challenges in Marathi language processing, including emotion-aware multimodal sentiment analysis and question answering initiatives. Learn about the efforts to enhance computational linguistics tools for Marathi content analysis.

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