  Dozens of countries have committed to restoring the ecological functionality
of 350 million hectares of land by 2030. In order to achieve such wide-scale
implementation of restoration, the values and priorities of multi-sectoral
stakeholders must be aligned and integrated with national level commitments and
other development agenda. Although misalignment across scales of policy and
between stakeholders are well known barriers to implementing restoration,
fast-paced policy making in multi-stakeholder environments complicates the
monitoring and analysis of governance and policy. In this work, we assess the
potential of machine learning to identify restoration policy agenda across
diverse policy documents. An unsupervised neural information retrieval
architecture is introduced that leverages transfer learning and word embeddings
to create high-dimensional representations of paragraphs. Policy agenda labels
are recast as information retrieval queries in order to classify policies with
a cosine similarity threshold between paragraphs and query embeddings. This
approach achieves a 0.83 F1-score measured across 14 policy agenda in 31 policy
documents in Malawi, Kenya, and Rwanda, indicating that automated text mining
can provide reliable, generalizable, and efficient analyses of restoration
policy.
