Evolutionary biology and historical linguistics share a long history of scientific exchange, reflected both not only in the sharing and transfer of metaphors but more recently also in the transfer of methods. Already Charles Darwin claimed that both species and languages evolve in tree-like patterns, and linguists, like Wilhelm Meyer-Lübke, used terms like “sprachliche[n] Biologie” (‘linguistic biology’) when referring to the history of languages (Meyer-Lübke 1890, x). While the discipline of historical-comparative linguistics allowed biologists to adopt evolutionary thought against religious dogma in the late 19th century (Wells 1987: 54), it was biological applications which opened up the possibility of large quantitative studies in linguistics based on computational approaches (Geisler and List 2013, 111).
The blog post introduces ideas discussed in our project about taking a closer look at word formation from a semantic (or semasiological) point of view. Since this so far underinvestigated approach to word formation processes lacks proper terminology, a new term to denote the central research question of concept-based type-frequency is introduced and contrasted with related established terminology.
In language comparison, there is a long history of using concept-based wordlists to get insights into the degree of similarity between languages, going back at least to Morris Swadesh (Swadesh 1950). For these purposes, words from different languages that share the same meaning are compared, either manually or with computational methods. The latter have the advantages of being both faster and more consistent. However, there are also limits to what computer-based methods can detect for the time being.
One of the biggest problems in this context is that none of the currently available methods for automatic cognate detection can infer partial cognates directly if no information on morpheme boundaries is provided by the user. As a result, if morpheme boundaries are missing and morphological differences are frequent in the data one wants to investigate, automatic cognate detection can be seriously hampered (List, Greenhill, and Gray 2017).