A neural information retrieval approach classifies forest and landscape restoration policy agenda in 31 policy documents with a reported 0.83 F1, though the evaluation is compromised by threshold tuning on the test data.
Chazdon, Pedro H
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Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval
A neural information retrieval approach classifies forest and landscape restoration policy agenda in 31 policy documents with a reported 0.83 F1, though the evaluation is compromised by threshold tuning on the test data.