Multi-word Expressions in E nglish Scientific Writing (2024)

Abstract: Multi-Word Expressions (MWEs) play a pivotal role in language use overall and in register formation more specifically, e.g. encoding field-specific terminology. Our study focuses on the identification and categorization of MWEs used in scientific writing, considering their formal characteristics as well as their developmental trajectory over time from the mid-17th century to the present. For this, we develop an approach combining three different types of methods to identify MWEs (Universal Dependency annotation, Partitioner and the Academic Formulas List) and selected measures to characterize MWE properties (e.g., dispersion by Kullback-Leibler Divergence and several association measures). This allows us to inspect MWEs types in a novel data-driven way regarding their functions and change over time in specialized discourse.

## introduction regularity in language does not only concern structural aspects (syntax, morphology), but also the way we combine words. some word combinations are perceived as patterns that are associated with specific meanings or connotations, collectively referred to as multi-word expressions (mwes). mwes range from idioms that are formally fixed and have a figurative meaning (e.g., kick the bucket) to compounds (bus ticket) or phrasal verbs (take a ride) that are typically compositional and often lexically fairly productive (cf. @xcite ). mwes are ubiquitous since they contribute to language efficiency by having highly predictable transitions from one word to the next or, if highly conventionalized, they can be retrieved from the lexicon rather than processed incrementally, thus giving them a processing advantage over other word sequences. furthermore, mwes play a crucial role in register formation as they provide conventional encodings of context-specific meanings. for example, mwes such as in no event or said therein are typical of legal language and rarely encountered elsewhere, while noun-noun and adjective-noun combinations such as iron oxide or sulphuric acid are a typical type of mwes used in scientific language forming domain-specific terminology. in particular, we are interested in mwes in scientific english from a diachronic perspective (mid 17th century to today). scientific english develops into a recognizable register during the late modern period and becomes highly conventionalized in modern times. overall, we want to better understand the process of register formation and whether processing advantages might have an impact. specifically, we ask (i) what are the mwes used in scientific english, (ii) which specific types of mwes are used in the scientific domain, and (iii) how to characterize the diachronic development of mwes in this domain, e.g., do specific mwes trend in particular periods, do mwes become more/less fixed and/or productive over time, thus contributing to conventionalization. for instance, we expect that mwes serving domain-specific terminology (such as noun-noun compounds) will be quite agile and productive, while others, e.g., expressions of stance (e.g. it is interesting) will change at a lower rate and be less varied. we develop an approach to identify mwes in scientific texts in order to be able to address these questions and better understand the role mwes play in register formation. the scientific domain is well-suited as it encompasses different types of mwes ranging from scientific terms up to stylistic expressions. in this paper, we take a first step towards answering the above questions, focusing on the identification of mwes and possibilities of categorization into types by applying dispersion and association measures to our diachronic data set. the remainder of the paper is organized as follows. in section 2 we discuss related work on mwes in computational linguistics/nlp. sections 3 and 4 presents our methods and results, including a preliminary diachronic analysis. we conclude with a summary and outlook (section 5). ## related work from a linguistic perspective, there are numerous corpus-based accounts of mwes in different registers, including the scientific one (e.g. @xcite ; @xcite ; @xcite ). some of these descriptions include lists of mwes used in academic texts that are freely available. we make use of one specific list @xcite in our own approach (section 3), but obviously such lists are always biased (time, corpus). therefore, a sound, reusable computational method for identifying mwes is needed, including analysis of their inherent properties (such as transparency, compositionality, fixedness; cf. also schulte im @xcite ). from a computational point of view, mwes have been considered "a pain in the neck" @xcite because they are not trivial to identify, let alone classify, by standard language models or nlp tools. two formal criteria of mwes are predictability in a given context (e.g., register) and relative fixedness of the elements within the expression. in combination with relative frequency, well-established measures to assess mwe candidates are pointwise mutual information (pmi) and log-likelihood, commonly also applied to identify collocations (e.g., @xcite ; @xcite ; @xcite ; @xcite ). regarding the identification of mwes using machine-learning methods, @xcite conducted a survey on existing mwe corpora and evaluation methods. they showed that most of the existing tools for this specific nlp task are based either on dimsum @xcite or parseme @xcite corpora and that, due to the complexity of the task and differences between approaches, results are not always comparable. parseme acknowledges discontinuity, variability, nesting, and overlaps and has a fine-grained mwe classification, however, it considers only verbal mwes. on the other hand, dimsum corpus is annotated for most major mwe categories (i.e., nominal, verbal, adverbial, and functional), but does not include any category labels. considering the scope of our study, the broader coverage of dimsum seems more relevant and aligned with our aims. the complexity of the automatic extraction of mwes is noticeable in works such as @xcite where state-of-the-art tools have f1-scores below 65. given the complexity of the task, different approaches focus on different aspects of mwes, so we decided to combine the state-of-the-art approaches for a more comprehensive treatment. ## conclusions and future work we presented a multifaceted approach to identify mwes in scientific english for analysing their evolution from the mid-17th century. our approach uniquely combines three distinct methods: (1) universal dependency annotation, which was key in uncovering syntactic structures of mwes, (2) partitioner, segmenting texts to detect mwes effectively, and (3) the academic formulas list, which further enriched our analysis by providing a benchmark for mwes used in the scientific context. our methodology went beyond identification; we used tools like kullback-leibler divergence for dispersion analysis and various association measures to characterise mwes (cf. gries (2022)). this revealed their dynamic nature and evolving roles in scientific discourse. some mwes adapted over time, reflecting changes in scientific language, while others remained consistent, signifying their entrenched role in scientific communication. our findings not only enhance understanding of mwes in scientific english but also pave the way for future linguistic research, particularly in language evolution and specialized registers. we currently work on integrating mwes in word embeddings to classify them semantically and model their temporal dynamics in terms of productivity. also, we intend to compute surprisal of mwes to link up with processing explanations (e.g. @xcite ; @xcite ).