The semantic map model is relatively new in linguistic research, but it has been intensively used during the past three decades for studying a variety of cross-linguistic and language-specific questions. The plethora of linguistic domains to which the model has been applied highlights its efficiency in capturing regular patterns of semantic structure and crosslinguistic similarities of form-meaning correspondence (for a complete list of domains, see van der Auwera & Temürcü, 2006: 132; Cysouw, Haspelmath, & Malchukov, 2010; Georgakopoulos & Polis, forthcoming). One of the advantages of the model is that any type of meaning can be integrated in semantic maps, such as the meanings or functions of grammatical morphemes, of entire constructions, or of lexical items, resulting in grammatical, constructional, and lexical semantic maps, respectively. However, it is fair to say that the different types of maps have not received equal attention in the literature. Rather, there is a strong bias towards studies describing cross-linguistic polysemies of grammatical morphemes and constructions. Additionally, the bulk of research using the semantic map method has been adopting a synchronic perspective and the limited research that has added the diachronic dimension has focused almost exclusively on the grammatical domain (e.g., van der Auwera & Plungian, 1998; Narrog, 2010). A notable common denominator of most of the studies is that the classical semantic maps have been plotted manually.
The aim of this talk is threefold. First, it will show that existing synchronic polysemy data in large language samples, such as CLICS (List et al., 2014) or the Open Multilingual Wordnet (http://compling.hss.ntu.edu.sg/omw/), can be converted into homogeneous lexical matrices using Python scripts. From these lexical matrices, one can infer large-scale weighted classical lexical semantic maps, using an adapted version of the algorithm introduced by Regier, Khetarpal, and Majid (2013). With this approach, we are able to automatically plot lexical semantic maps from a significant amount of cross- linguistic data. These maps are structured respecting the connectivity hypothesis (Croft, 2001) and what we call the ‘economy principle’. As such, they generate more interesting implicational universals than regular colexification networks and can be falsified based on additional empirical evidence.
Second, this talk seeks to demonstrate how information on the paths of semantic extensions undergone by content words may be incorporated into a semantic map. In order to illustrate the method, we take the example of the semantic extension of time-related lexemes (e.g. TIME , HOUR , SEASON , DAY ) in Ancient Greek (8th – 1st c. BC) and Ancient Egyptian – Coptic (26th c. BC – 10th c. AD). Both languages give access to significant diachronic material, allowing us to trace long term processes of semantic change. This diachronic take on the polysemic networks of content words has a methodological bearing on the model, since it serves as a compass on how to plot automatically diachronic semantic map.
Third, the talk will illustrate how the automatically plotted semantic maps can be examined using standard network exploration systems. These tools, with many built-in statistical methods, reveal much information otherwise ‘hidden’ in the graph — such as the modularity of the network, the centrality of the meanings, etc. — and are essential when it comes to interpreting large- scale crosslinguistic datasets. The potentialities in this area will be illustrated throughout the talk.
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van der Auwera, J., & Temürcü, C. (2006). Semantic Maps. In K. Brown (Ed.), Encyclopedia of language and linguistics (2 nd ed.), vol. 11, (pp. 131–134). Oxford: Elsevier.
Georgakopoulos, T., & Polis, S. (forthcoming). The semantic map model: State of the art and future avenues for linguistic research. Language & Linguistics Compass.
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Regier, T., Khetarpal, N., & Majid, A. (2013). Inferring semantic maps. Linguistic Typology, 17(1), 89–105. doi:10.1515/lity-2013-000