Sterner & Tale – Explaining Ambiguity in Scientific Language – DS² 2021
Channel: CEFISES at UCLouvain
Date: 2021-03-18

The talk “Explaining ambiguity in scientific language: towards a computational approach” by Beckett Sterner and Ankush Tale (Arizona State University), recorded at the DS² 2021 online conference, March 17, 2021.



The when, where, how, and why of productive ambiguity in data-centric science remains an important source of controversy. In order to facilitate the growth of data-intensive research, scientists are investing massive effort into constructing formal language systems with standardized, context-invariant meanings for each concept (Leonelli 2016). The Open Biomedical Ontology (OBO) Foundry is just one example among many (Smith et al. 2007; Smith and Ceusters 2010). A basic assumption behind these efforts is that allowing technical terms to have multiple, conflicting meanings undermines scientific reasoning and communication (Sterner et al. 2020). However, the principle that scientific language should be maximally determinate has been highly divisive, as many researchers strongly support pluralistic approaches, especially in the fields of history and philosophy of science (HPS) and science and technology studies (STS). On this view, instead of pigeonholing ambiguity as a categorical vice that scientists should seek to eliminate at all cost, we need to understand when and why certain forms of ambiguity have valuable roles to play in scientific practice (Gross 2006).

In this paper, we distinguish two theoretical ideals for scientific language that differ on the value of ambiguity and the appropriate method for managing it. Critically, we describe each ideal so as to have a characteristic empirical signature that can be detected in scientific discourse. Given these competing alternatives, a pressing issue is then determining which ideal is most apt for some situation at hand, with special attention to the needs of data-centric science today. Rapid advances in Natural Language Processing (NLP) methods are making it increasingly feasible to discover and correlate the fine-grained semantics for terms of interest, their relevant synonyms, and the context types in which those synonyms can be reliably substituted without affecting overall sentence meaning (Tahmasebi et al. 2019).

We also present new empirical results using contextual embeddings from BERT (Devlin et al. 2019) to uncover these dimensions of scientific language using automated clustering in combination with expert semantic analysis. We illustrate our approach using a study of contested and partially synonymous terms biologists use to identify and describe scientifically significant populations within species, such as "subspecies," "variety," and "ecotype." Our dataset comes from a historical corpus of about 500,000 biological texts from JSTOR spanning roughly 150 years, though our main results currently focus on articles published before 1900. Subspecies in particular are important for conservation and may receive legal protection under laws such as the Endangered Species Act. Evolutionary biologists have also used subspecies for well over a century to collect and communicate information about organismal variation relevant to testing theories of speciation. We conclude by relating our approach and results to recent advances in NLP over the past two years and characterize how these are opening new opportunities for using digital corpus data to address when and how we should seek to accommodate ambiguity in future big data research.