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There has been several reports in the Nigerian and International media aboutthe Senators and House of Representative Members of the Nigerian NationalAssembly (NASS) being the highest paid in the world. Despite this high-level ofparliamentary compensation and a lack of oversight, most of the legislativeduties like bills introduced and vote proceedings are shrouded in mysterywithout an open and annotated corpus. In this paper, we present results fromongoing research on the categorization of bills introduced in the Nigerianparliament since the fourth republic (1999 - 2018). For this task, we employeda multi-step approach which involves extracting text from scanned and embeddedpdfs with low to medium quality using Optical Character Recognition (OCR) toolsand labeling them into eight categories. We investigate the performance ofdocument level embedding for feature representation of the extracted textsbefore using a Bidirectional Long Short-Term Memory (Bi-LSTM) for ourclassifier. The performance was further compared with other featurerepresentation and machine learning techniques. We believe that these resultsare well-positioned to have a substantial impact on the quest to meet the basicopen data charter principles.

NASS-AI: Towards Digitization of Parliamentary Bills using Document Level Embedding and Bidirectional Long Short-Term Memory