Natural Language Processing (NLP) is a broad umbrella of technologies used for computationally studying large amounts of text and extracting meaning - both syntactic and semantic information. Software using NLP technologies, if engineered for that purpose, generally have the advantage of being able to process large amounts of text at rates greater than humans. A large number of the functions of a government today revolve around vast amounts of text data - from interactions with citizens to examining archives to passing orders, acts, and bylaws. Under ideal conditions, NLP technologies can assist in the processing of these texts, thus potentially providing significant improvements in speed and efficiency to various departments of government. Many proposals and examples exist illustrating how this can be done for multiple domains - from registering public complaints, to conversing with citizens, to tracking policy changes across bills and Acts. This whitepaper seeks to examine both the current state of the art of NLP and illustrate government-oriented use cases that are feasible among resource-rich languages.
Keywords: Natural Language Processing | Machine Learning / Deep Learning | Big Data | Sinhala | Low-resource Languages | Tamil | Data Mining | Public Policy |
@article{Wijeratne2019Natural,
Author={Wijeratne, Yudhanjaya and de Silva, Nisansa and Shanmugarajah, Yashothara},
title={{Natural Language Processing for Government: Problems and Potential}},
year={2019},
journal={LIRNEasia},
abstract={Natural Language Processing (NLP) is a broad umbrella of technologies used for computationally studying large amounts of text and extracting meaning - both syntactic and semantic information. Software using NLP technologies, if engineered for that purpose, generally have the advantage of being able to process large amounts of text at rates greater than humans. A large number of the functions of a government today revolve around vast amounts of text data - from interactions with citizens to examining archives to passing orders, acts, and bylaws. Under ideal conditions, NLP technologies can assist in the processing of these texts, thus potentially providing significant improvements in speed and efficiency to various departments of government. Many proposals and examples exist illustrating how this can be done for multiple domains - from registering public complaints, to conversing with citizens, to tracking policy changes across bills and Acts. This whitepaper seeks to examine both the current state of the art of NLP and illustrate government-oriented use cases that are feasible among resource-rich languages.},
doi={10.13140/RG.2.2.34297.31845},
misc={http://bit.ly/2RyVtF9,NLP:ML:BIG:SIN,https://i1.rgstatic.net/publication/333968711_Natural_Language_Processing_for_Government_Problems_and_Potential/links/5d61b7f4458515d610227e8e/largepreview.png,http://bit.ly/2FHtyxZ}
}
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.
ClosedPI: Rohan Samarajiva
We propose to investigate the role of natural language processing in government by building methods, frameworks, and resources that enable data-driven policy-making while ensuring ethical, transparent, and inclusive outcomes.
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.