Sanskrit Sandhi Splitting using seq2(seq)^2

Aralikatte, Rahul; Gantayat, Neelamadhav; Panwar, Naveen; Sankaran, Anush; Mani, Senthil

doi:10.48550/arxiv.1801.00428

Cited by 1 publication

(5 citation statements)

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“…The model achieves a sentence splitting and string splitting accuracy of 85.2% and 96.7%, respectively. Aralikatte et al (2018a) presented a double decoder (DD-RNN) with an attention model for the word decompounding in Sanskrit. They found that the model provides the splitting location and prediction accuracy of 95% and 79.5%, respectively, which outperforms the state of art by 20%.…”

Section: Corpus-based Decompounding Methodsmentioning

confidence: 99%

“…Hence, we evaluated different decompounding techniques in Indian languages in the IR domain. Our work is motivated by the earlier work of Koehn and Knight (2003), Ganguly et al (2013), Ajees and Graham (2018), Aralikatte et al (2018a).…”

Section: Deep Learning-based Decompounding Methodsmentioning

confidence: 99%

“…Different researchers proposed and evaluated different decompounding models such as corpus-based (Adda-Decker 2003; Deepa et al 2004;Ganguly et al 2013), machine learning based Ajees and Graham (2018), and deep learning-based (Hellwig and Nehrdich 2018;Aralikatte et al 2018a;Hellwig 2015) in Asian and European languages. Among the different decompounding models, they observed that the corpus-based decompounding models perform poorly in morphological-rich Indian languages.…”

Section: Introductionmentioning

confidence: 99%

“…Different machine learning-based models improve effectiveness in morphologically rich languages. In recent years, the deep learningbased models outperform the corpus-based decompounding models in Sanskrit (Dave et al 2021;Hellwig and Nehrdich 2018;Aralikatte et al 2018a;Hellwig 2015).…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, no previous studies exist on the effect of decompounding models in Indian languages IR Koehn and Knight (2003), Ajees and Graham (2018), Aralikatte et al (2018a). This study explores the impact of different decompounding models, i.e.…”

Section: Introductionmentioning

confidence: 99%

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A case study on decompounding in Indian language IR

Sahu,

Pal

2024

Nat. lang. processing

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Decompounding is an essential preprocessing step in text-processing tasks such as machine translation, speech recognition, and information retrieval (IR). Here, the IR issues are explored from five viewpoints. (A) Does word decompounding impact the Indian language IR? If yes, to what extent? (B) Can corpus-based decompounding models be used in the Indian language IR? If yes, how? (C) Can machine learning and deep learning-based decompounding models be applied in the Indian language IR? If yes, how? (D) Among the different decompounding models (corpus-based, hybrid machine learning-based, and deep learning-based), which provides the best effectiveness in the IR domain? (E) Among the different IR models, which provides the best effectiveness from the IR perspective? This study proposes different corpus-based, hybrid machine learning-based, and deep learning-based decompounding models in Indian languages (Marathi, Hindi, and Sanskrit). Moreover, we evaluate the effectiveness of each activity from an IR perspective only. It is observed that the different decompounding models improve IR effectiveness. The deep learning-based decompounding models outperform the corpus-based and hybrid machine learning-based models in Indian language IR. Among the different deep learning-based models, the Bi-LSTM-A model performs best and improves mean average precision (MAP) by 28.02% in Marathi. Similarly, the Bi-RNN-A model improves MAP by 18.18% and 6.1% in Hindi and Sanskrit, respectively. Among the retrieval models, the In_expC2 model outperforms others in Marathi and Hindi, and the BB2 model outperforms others in Sanskrit.

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Section: Corpus-based Decompounding Methodsmentioning

confidence: 99%