Sinhala, despite its several millennia long history, remains a resource poor language. The objective of this study was to explore the possibility of enhancing the text classification process of a resource poor language by means of data and tools from a resource rich language. However, it was discovered that if the feature space is based on an n-gram model, Sinhala, being a a highly inflected language, naturally performs better than English, which is a weakly inflected language. This result held true even when Sinhala was only utilizing the basic lexical level models and English was utilizing advanced semantic level models.
Keywords: Natural Language Processing | Machine Learning / Deep Learning | Ontologies | Big Data | Sinhala | Text Classification | Low-resource Languages | High-resource Languages |
@article{de2015Sinhala,
Author={de Silva, Nisansa},
title={{Sinhala Text Classification: Observations from the Perspective of a Resource Poor Language}},
year={2015},
journal={},
abstract={Sinhala, despite its several millennia long history, remains a resource poor language. The objective of this study was to explore the possibility of enhancing the text classification process of a resource poor language by means of data and tools from a resource rich language. However, it was discovered that if the feature space is based on an n-gram model, Sinhala, being a a highly inflected language, naturally performs better than English, which is a weakly inflected language. This result held true even when Sinhala was only utilizing the basic lexical level models and English was utilizing advanced semantic level models.},
misc={https://goo.gl/ZyQHjj,NLP:ML:ONT:BIG:SIN,https://i1.rgstatic.net/publication/329908360_Sinhala_Text_Classification_Observations_from_the_Perspective_of_a_Resource_Poor_Language/links/5c2214a0299bf12be39857ac/largepreview.png}
}
ActivePI: Nisansa de Silva
The Knowledge Representation project investigates methods for structuring, aligning, and reasoning over knowledge extracted from language, with particular emphasis on low-resource settings, domain-specific semantics, and intelligent information access.
ActivePI: Nisansa de Silva
We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.
ActivePI: Nisansa de Silva
We propose to build and analyze Sinhala language resources, datasets, and models to strengthen NLP research for this low-resource language, while connecting it to English and Tamil through bilingual lexicons, embeddings, and cross-lingual methods.