Tracing Definitions: Lessons from Alliance Contracts in the Biopharmaceutical Industry (2025)

Abstract: 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.

## introduction collaboration between firms is a crucial building block for a globalized economy. contracts, i.e., legal agreements that determine promises, obligations, and the future course of action @xcite , constitute the backbone of alliances as organizational forms of collaboration. a key element within such legal documents that shapes the interpretation of the contract is definitions, i.e., exact descriptions of key terms used in the contract. the crucial function of definitions-and their potential to create costly misunderstandings-became evident to a broader audience in august 2020, when the european commission and astrazeneca included a contractual definition of astrazeneca's 'best reasonable efforts' for vaccine supply. despite this, the alliance partners apparently did not agree on what the term meant, as this became the subject of a long and costly high-profile legal dispute halting the collaboration for nearly half a year. after the litigation had escalated, the partners eventually "[. . . ] have been able to reach a common understanding which allows @xcite to move forward and work in collaboration [. . . ]". 1 given that these contracts are texts that constitute the foundation of alliances, they present a complex and unique research setting at the intersection of nlp, data science, and economics. thus, in this paper, we set out to study a large corpus of alliance contracts through a unique lens, i.e., by tracing and analyzing the definitions used. in doing so, we can automatically find unusual -and therefore potentially risky -definitions of terms, identify topical areas covered by definitions in alliance contracts, and can observe trends in terms defined in the contracts over time. in this paper, we study a corpus of 12,468 alliance contracts from the biopharmaceutical industry. we begin by extracting texts-specifically definitions-from these contracts, comparing different automated methods in the process. this extraction process resulted in a corpus of 380,131 definitions. by analyzing the similarity of definitions using embeddings, we then demonstrate, for instance, that the disputed 'best reasonable effort' definition in the astrazeneca-ec case was highly dissimilar and unusual. using topic modeling, we identify key content categories of definitions. to further trace trends in definitions over time, we leverage our dataset's longitudinal design, which includes alliance contracts signed between 2000 and 2020. our main contribution is the application of nlp methods to real-world contract texts. in so doing, we demonstrate accurate and efficient methods for extracting and analyzing contract texts. furthermore, we contribute by providing a comprehensive dataset-a definition corpus. beyond these methodological and data-related contributions, we advance the literature in two ways: first, our findings reveal that legal definitions are the most similar across alliance contracts, whereas technological and social definitions show more variation, making them more dissimilar, unusual, and specific to each alliance. therefore, alliance partners should pay special attention to those definitions. second, we demonstrate that external events, such as the covid-19 pandemic and financial market shocks, coincide with shifts in the usage and the content of contract definitions. this finding expands the traditional view of contracts as predominantly static legal instruments, instead emphasizing their role as adaptable tools embedded in the evolving dynamics of their broader environment. for instance, we show that while alliance partners most often define technological terms-such as product-and patentrelated terms-in their contracts, stock-related definitions have become more common over the years. ## related work from a linguistic standpoint, definitions formally ascribe meaning to an undefined term (i.e., the definiendum) using already established terms (i.e., definiens). an example illustrating this terminology is shown in figure 1 . by defining a term that is yet to be defined, definitions explicitly create a shared understanding among members who agree on the definition. in alliances, definitions set up in their contracts thus establish the meaning of terms relevant to the signing parties. given the importance of contract text and the definitions therein, existing ai research on automatically examining definitions is limited, primarily focusing on isolated definitions or specific terms. while this has generated a wealth of insights, as, for instance, legg and hutter in 2007, collect and compare 70 definitions of the word 'intelligence' @xcite , it is limited because definitions are often embedded in real-world texts, such as alliance contracts. extracting information embedded within the broader contract text and accounting for variations in document layouts across real-world texts is challenging. while sentence boundary detection (sbd) on curated data achieved accuracy scores of up to 99% as early as 2012 @xcite , attaining such accuracy of sbd in noisy text data extracted from pdfs is an underexplored problem. recent advances in the field are made by a series of shared tasks called finsbd @xcite @xcite . the most successful method utilized two neural architectures, bilstm-crf and bert, and took into account both visual cues and textual data, achieving a mean f1-score of 0.937 on sentences and 0.844 on lists @xcite . although this method can be taken as a proof of concept for extracting text from noisy pdfs, this approach does not scale for thousands of contracts. in turn, most work in the field focuses on specific information, which is easier to extract, such as the alliance partners of a contract @xcite . extracting specific terms and meanings from contracts still largely depends on expensive expert annotation usually restricted to a subset of terms; for example, @xcite annotated a subset of 41 categories. ## review of methods surveys of text representation techniques indicate that transformer-based embeddings currently perform best to compare semantic aspects between texts at scale in an unsupervised manner @xcite . thus, we selected two well-studied transformer-based sentence encoding methods. first, we relied on sentence-bert (sbert) @xcite , the most established model in prior literature. more specifically, we used the pretrained model all_mpnet_base_v2, which has achieved the highest overall quality in benchmarks foot_1 among sbert models. as a more recent model, we choose the general text embeddings (gte) model @xcite , as its largest variation gte-large was one of the best performing, freely available models on the massive text embedding benchmark @xcite . we follow recent approaches of using clustered word embeddings as topic models @xcite . we utilize the bertopic framework @xcite , which uses classbased tf-idf to identify meaningful topics in the clusters. to ensure robustness of the topics, we use multiple dimensionality reduction algorithms, mainly pca @xcite and umap @xcite , as well as clustering methods @xcite , birch @xcite , @xcite ) in combination. ## contract corpus and definition extraction we collected 18, 742 alliance contracts in the biopharmaceutical industry signed between 1973 -2021 in pdf format retrieved from bioscience advisors (now part of evaluate), which consolidates information from the sec and the freedom of information act. this is structurally equivalent to the widely used recap database (now cortellis deals intelligence; @xcite ). before starting with our analysis, we conducted extensive manual cleaning efforts to ensure accuracy: removing duplicates, supplementing missing contracts via manual searches through law insider and the sec, coding ancillary firm-level data, including headquarters and founding year from corporate websites, standardizing firm names, and accounting for name changes and parent-subsidiary relationships. after collecting the contract texts in the form of pdf documents, we perform three steps to extract the definitions. first, we use the open source pypdf2 python package @xcite to convert pdf documents to text. generally, such tools generate noisy, unstructured texts containing nonstandard words, false starts, missing punctuation, missing letter case information, and other text disfluencies @xcite , ultimately complicating the extraction of correct text passages. therefore, we perform cleaning steps with regex to standardize whitespaces or tabs to one space length and remove multiple newlines in a row. second, we use three different methods to extract definitions. the first method to extract is based on multiple open source sbd tools, combined with a simple check for common words alliance partners use in definitions. the second method is based on a regular expression (regex) that searches for common words, common structure, and phrases of definitions. the third method is based on the large language model llama-3.1-70b-instruct of the llama 3 model family @xcite . the regex and large language model method are explained in more detail in appendix section a. third, we use a subset annotated by human coders to evaluate and compare the three extraction methods. three independent coders, one author and two thesis students, all equipped with knowledge of the seminal alliance contract literature and information retrieved from interviews with two practitioners (more specifically, one alliance manager and one lawyer), identified and counted definitions in 826 alliance contract pdfs without predefined coding guidelines to maintain an openended qualitative approach. in rare cases of disagreement among the coders about the number of definitions, this was resolved through mutual discussion after completion of their individual coding efforts. in this subset, we have both contracts with zero definitions and larger contracts containing up to 735 definitions. because of this large variance and contracts that are hard to extract data from due to the noisy pdf reading, we evaluate the methods by their median count error per contract against the human counts. the sbd approach finds fewer definitions than the human-annotated dataset and has a median error rate of 6 definitions per contract. additionally, this method fails to identify definitions in full length when the definition text spanned multiple sentences. in comparison, the regex method finds slightly more definitions than the human count and has a median error rate of 3 definitions per contract. although the llm method proved effective for shorter contracts, longer contracts introduced significant drawbacks. even when setting the temperature to 0, the llm still hallucinates, i.e., includes definitions that are not written in the realworld contract text. similar problems are known for extracting facts from documents @xcite , where pretraining data is returned instead of document data and even larger models struggle to accurately return document facts, once the context size increases significantly @xcite . overall, the llm method has a median error rate of 5 definitions per contract. based on this comparison, we conclude to use the regex method for extracting definitions from the whole corpus. using regex, we identify a total of 457,711 definitions across 12,468 contracts. in so doing, we identify 122,414 different definienda in the contract corpus. for the following analysis, we drop 77,482 definitions containing the phrase 'set forth in', which indicates that the definition is defined in another chapter of the contract. this leaves a final sample of 380,131 definitions. we release these definitions as a dataset 3 containing a unique id for each alliance contract, the year the contract was signed, and the definition itself, which is split into the definiendum and the definiens. for the published dataset, we use a regex to remove all company names from the text and replace them with the placeholder @xcite . for the following analysis, these definitions were then preprocessed by removing all line breaks, standardizing whitespaces, and removing all non-alphanumeric characters. ## similarity of definitions after identifying regular expressions as the most accurate method for extracting definition text from contracts, we aim to analyze the similarity of definitions. specifically, we examine definitions with the same definiendum to understand which definitions are written in a standardized manner and which vary significantly. to do so, we select all definienda that appear in at least 100 different contracts (when converted to lower case), resulting in 369 definienda. after removing four redacted definienda (usually for confidentiality reasons), we retain 365 different definienda for further analysis. for example, the most usual definiendum, 'affiliate', is frequently defined (precisely in 6,584 different contracts). next, we create the embeddings solely on the definientia. then, we calculate the mean of all pairwise cosine similarity scores between all definientia that have the same definiendum. we thus obtain a mean similarity score for each definiendum, where a high score indicates that definitions of a certain definiendum are defined more similarly across contracts, and a low score indicates the reverse. in the following, we compared similarity across all definienda by relying on three inductively produced overarching categories of definitions (gioia @xcite . in so doing, in a first step, we manually reviewed each contract definition individually and assigned first-order concepts, i.e., descriptive codes closely reflecting the wording in the data. after reviewing 74 contracts in this way, we aggregated these into second-order concepts by clustering related first-order concepts and abstracting their underlying themes. finally, we distilled these second-order concepts into three mutually exclusive aggregate categories -social, technological, and organizational definitions. the most similarly defined definienda are legal terms like 'irs' (i.e., internal revenue service) or 'ema' (i.e., european medicines agency). on the other hand, the most dissimilar definienda are specific to an alliance, e.g., the parties involved in the contract. for qualitative analysis, definienda which differ slightly across contracts are interesting, e.g. 'diligent efforts', which we analyze in section 5.2. although individual score differences between the two embedding methods are substantial, the methods exhibit a strong overall rank correlation. the spearman rank correlation between the two methods is approximately 0.96, and the kendall tau correlation is approximately 0.84. the higher scores of gte might indicate that gte embeds the sentences in a similar embedding space, as they are all definitions, which sbert might not do. however, as we only compare definitions with each other, this has little influence on the general ranking and further results are similar. for this reason, examples in this paper will only be shown when computed on gte embeddings. ## change of definitions over time next, we investigate how contract definitions change over time. we first assess if there are definitions that have become more prevalent in general or specific terms (definienda) whose meaning (definiens) has changed over time. second, we create topic models to identify prevalent topics and their trends. ## conclusion overall, this paper applies nlp methods to a unique corpus, whose central role in forming alliances makes them highly relevant for both practice and research. specifically, we extracted definitions from real-world alliance contract texts in the biopharmaceutical industry at scale. first, methodologically, we develop a tool set that allows us to study definitions in text corpora using a combination of traditional tools and modern machine learning approaches. more specifically, we find that regex seems most suitable for extracting definitions from noisy real-world contract text. second, we contribute to the research community studying computational processing of language by publishing a comprehensive dataset of definitions, distinguishing between definienda and definientia in alliance contracts over time. third, this interdisciplinary study also hopes to make two theoretical contribu-tions to existing research. while our results show that the ascribed meaning to social terms are most dissimilar and social definienda are most unusual (i.e., they differ the most in meaning and usage), our method allows to derive whether a certain definition differs from the most usual way it is defined across alliance contracts. this, in turn, could provide alliance partners with the ability to identify unusual definitions or dissimilarly defined terms, allowing them to focus on those definitions in negotiations to navigate potential conflicts early on. in addition, we analyze definitions in alliance contracts signed between 2000 and 2021 and find that alliance partners generally use more definitions over time. although the number of definitions used per contract increased over time, the content of definitions is embedded in the broader environmental context; for instance, stock-related definitions have become more common after the financial crisis. the latter extends more traditional views on alliance contracts, which have mainly focused on the transaction itself, by interpreting them as embedded in larger economic surroundings. in conclusion, in applying nlp methods to real-world corpora, our paper integrates qualitative insights with quantitative analysis. while we acknowledge that qualitative information has inherent limitations -for instance in terms of generalizability -our study postulates and aims to showcase that such humancentered qualitative analyses support, contextualize and embed nlp findings in a meaningful way, particularly in context-rich texts such as alliance contracts.