Beyond modeling: NLP Pipeline for efficient environmental policy
analysis
- URL: http://arxiv.org/abs/2201.07105v1
- Date: Sat, 8 Jan 2022 05:33:04 GMT
- Title: Beyond modeling: NLP Pipeline for efficient environmental policy
analysis
- Authors: Jordi Planas, Daniel Firebanks-Quevedo, Galina Naydenova, Ramansh
Sharma, Cristina Taylor, Kathleen Buckingham, Rong Fang
- Abstract summary: Policy analysis is necessary for policymakers to understand the actors and rules involved in forest restoration.
We propose a Knowledge Management Framework based on Natural Language Processing (NLP) techniques.
We describe the design of the NLP pipeline, review the state-of-the-art methods for each of its components, and discuss the challenges that rise when building a framework oriented towards policy analysis.
- Score: 0.6597195879147557
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: As we enter the UN Decade on Ecosystem Restoration, creating effective
incentive structures for forest and landscape restoration has never been more
critical. Policy analysis is necessary for policymakers to understand the
actors and rules involved in restoration in order to shift economic and
financial incentives to the right places. Classical policy analysis is
resource-intensive and complex, lacks comprehensive central information
sources, and is prone to overlapping jurisdictions. We propose a Knowledge
Management Framework based on Natural Language Processing (NLP) techniques that
would tackle these challenges and automate repetitive tasks, reducing the
policy analysis process from weeks to minutes. Our framework was designed in
collaboration with policy analysis experts and made to be platform-, language-
and policy-agnostic. In this paper, we describe the design of the NLP pipeline,
review the state-of-the-art methods for each of its components, and discuss the
challenges that rise when building a framework oriented towards policy
analysis.
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