Natural Language Processing for Government: Problems and Potential

Yudhanjaya Wijeratne, Nisansa de Silva, Yashothara Shanmugarajah
LIRNEasia

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 |