  There has been several reports in the Nigerian and International media about
the Senators and House of Representative Members of the Nigerian National
Assembly (NASS) being the highest paid in the world. Despite this high-level of
parliamentary compensation and a lack of oversight, most of the legislative
duties like bills introduced and vote proceedings are shrouded in mystery
without an open and annotated corpus. In this paper, we present results from
ongoing research on the categorization of bills introduced in the Nigerian
parliament since the fourth republic (1999 - 2018). For this task, we employed
a multi-step approach which involves extracting text from scanned and embedded
pdfs with low to medium quality using Optical Character Recognition (OCR) tools
and labeling them into eight categories. We investigate the performance of
document level embedding for feature representation of the extracted texts
before using a Bidirectional Long Short-Term Memory (Bi-LSTM) for our
classifier. The performance was further compared with other feature
representation and machine learning techniques. We believe that these results
are well-positioned to have a substantial impact on the quest to meet the basic
open data charter principles.
