Definitions in alliance contracts play a critical role in shaping agreements, yet they can also lead to costly misunderstandings. This is exemplified by the multimillion-dollar AstraZeneca-Euopean Commission (EC) dispute, where the interpretation of ‘best reasonable effort’ became the focal point of contention. In this interdisciplinary study, we leverage natural language processing (NLP) to systematically analyze patterns in the definitions included in alliance contracts. More specifically, we categorize the content of definitions into topics, identify common terms versus outliers that are semantically dissimilar and infrequently used, and track how definitions evolve over time. Analyzing a dataset of 380,131 definitions from 12,468 alliance contracts in the biopharmaceutical industry, we distinguish that definitions span legal, technological, and social topics, with social terms showing the highest dissimilarity across contracts. Using dynamic topic modeling, we explore how the content of definitions has shifted over two decades (2000–2020) and identify prevalent trends suggesting that contractual definitions reflect broader economic contexts. Notably, our results reveal that the AstraZeneca-EC dispute arose from an outlier—a highly unusual definition—that could have been flagged using NLP. Overall, these findings highlight the potential of data-driven approaches to uncover patterns in alliance contracts.