A Hybrid Approach for Suffix Separation in Malayalam

Ajees, A P; Graham, George

doi:10.1109/icetietr.2018.8529103

Cited by 1 publication

(4 citation statements)

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“…Hence, they built self-supervised models that outperformed the unsupervised decompounding models and improved accuracy by 13.9%. Ajees and Graham (2018) presented a hybrid decompounding method in Malayalam. The decompounding method integrates a rule-based and machine-learning approach.…”

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%

“…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%