In wrapping up the first year of LDNA, I’ve taken a moment to consider some of the over-arching questions that have occupied much of my creative and critical faculties so far. What follows is a personal reflection on some issues that I’ve found especially exciting and engaging.
Semantics and concepts
The Linguistic DNA project sets out to identify ‘semantic and conceptual change’ in Early Modern English texts, with attention to variation too, particularly in the form of semantic and conceptual variation across text types. The first questions, for me, then, were what exactly constitutes semantics and what we mean when we say concept. These are, in part, abstract questions, but they must also be defined in terms of practical operations for computational linguistics. Put differently, if semantics and concepts are not defined in terms of features that can be identified automatically by computer, then the definitions are not terribly useful for us.
My first attempt at approaching semantics and concepts for the project began with synonymy, then built up to onomasiological relationships, and then defined concepts as networks of onomasiological relationships. Following Kris Heylen’s visit, I realised just how similar this approach was to the most recent QLVL work. My next stab at approaching these terms moved towards an idea of encyclopaedic meaning inspired in part by the ‘encyclopaedic semantics’ of Cognitive Linguistics, and related to sets of words in contexts of use. This approach seemed coherent and effective. We have since come to define concepts, for our purposes, as discursive, operating at a level larger than syntactic relations, phrases, clauses, or sentences, but smaller than an entire text (and therefore dissimilar from topic modelling).
Given that the project started without a definition of semantics and concept, it follows that the operationalisation of identifying those terms had not been laid out either. As a corpus semanticist, the natural start for me was to sort through corpus methods for automatic semantic analysis, including collocation analysis, second-order collocations, and vector space models. We continue to explore those methods by sorting through various parameters and variables for each. Most importantly, we are working to analyse our data in terms of linguistically meaningful probabilities. That is, we are thinking about the co-occurrence of words not simply as data points that might arise randomly, but as linguistic choices that are rarely, if ever, random. This requires us to consider how often linguistic events such as lexical co-occurrences actually arise, given the opportunity for them to arise. If we hope to use computational tools to learn about language, then we must certainly ensure that our computational approaches incorporate what we know about language, randomness, and probability.
Equally important was the recognition that although we are using corpus methods, we are not working with corpora, or at least not with corpora as per standard definitions. I define a corpus as a linguistic data-set sampled to represent a particular population of language users or of language in use. Corpus linguists examine language samples in order to draw conclusions about the populations they represent. EEBO and ECCO are, crucially, not sampled to represent populations—they are essentially arbitrary data sets, collected on the basis of convenience, of texts’ survival through history, and of scholarly interest and bias, among other variables. It is not at all clear that EEBO and ECCO can be used to draw rigorous conclusions about broader populations. Within the project, we often refer to EEBO and ECCO as ‘universes of printed discourse’, which renders them a sort of population in themselves. From that perspective, we can conclude a great deal about EEBO and ECCO, and the texts they contain, but it is tenuous at best to relate those conclusions to a broader population of language use. This is something that we must continually bear in mind.
Rather than seeing the LDNA processor as a tool for representing linguistic trends across populations, I have recently found it more useful to think of our processor primarily as a tool to aid in information retrieval: it is useful for identifying texts where particular discursive concepts appear. Our tools are therefore expected to be useful for conducting case studies of particular texts and sets of texts that exemplify particular concepts. In a related way, we use the metaphor of a topological map where texts and groups of texts exemplifying concepts rise up like hills from the landscape of the data. The processor allows us to map that topography and then ‘zoom in’ on particular hills for closer examination. This has been a useful metaphor for me in maintaining a sense of the project’s ultimate aims.
All of these topics represent ongoing developments for LDNA, and one of the great pleasures of the project has been the engaging discussions with colleagues about these issues over the last year.