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- W2034205503 abstract "This paper develops a protocol for using a familiar data set on force majeure provisions in corporate acquisitions agreements to tokenize and calibrate a machine-learning algorithm of textual analysis. Our protocol, built on regular expression (RE) and latent semantic analysis (LSA) approaches, serves to replicate, correct, and extend the hand-coded data. Our preliminary results indicate that both approaches perform well, though a hybridized approach improves predictive power further. Monte Carlo simulations suggest that our results are generally robust to out-of-sample predictions. We conclude that similar approaches could be used more broadly in empirical legal scholarship, especially including in business law." @default.
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- W2034205503 date "2012-01-01" @default.
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- W2034205503 title "The Measure of a MAC: A Machine-Learning Protocol for Analyzing Force Majeure Clauses in M&A Agreements" @default.
- W2034205503 doi "https://doi.org/10.1628/093245612799440177" @default.
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