Linguistics is often questioned by practitioners of Natural Sciences during informal and professional scenarios. Perhaps, this is the case due to the fact that the phenomena relevant to them is more readily observable through technical methodologies that are better defined. For instance, a casual (as in an individual who casually has become acquainted with some little morsel of data about a language) may define Linguistics as just ‘adding an ‘s’ to the end of a word to make it plural.
Similarly, asking musicians to recite every note on a scale (which many professionally trained or classically inclined enjoy doing) does not guarantee they can play well to a live audience. So, if the casual definition for Linguistics is graining ground, the priorities for the field in terms of rationale and problem solving is too centered on archiving data that exists without an explanation (data without science, basically).
Trivia In The Office
Figuring out when some rule from Oxford applies in a romance novel is fine trivia, but it is not ‘Linguistics’. To begin with, the scientific practice around explaining language behavior is very broad and interdisciplinary. We should not permit the discipline to become describing the inscriptions literally.
While data and rules about languages are important, memorizing data feels like a somewhat pointless exercise. This is a sign that the field in some corners is overly defined by linguistic trivia about English – or some other pet language – rather than in terms of reproducible general principles that can be easily computed.
Linguistics Takes Time
Currently, my fear is that as Linguistics in the enterprise context gains strength, the finer points around rationale will be overtaken by boring data recitals. If so, we are in for a world of trivia and not developments largely as a result from the influence of non-linguist priorities in the discipline. The drive to subvert some computational tools for linguistic ends does not exist.
Narrative is very important. Understanding why a linguistic analysis helps ground activities. This ability to contribute to NLP activities through adopting a proper narrative for linguistic activities in the enterprise setting has not surfaced beyond the ‘we need better data for this hungry machine learning algorithm’. It pays the bills, but it does not advance the field.