A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Crosslinguistic Case Study of Supplementary Adverbs
This addresses the problem of time-consuming manual modeling for linguists and researchers in cross-linguistic semantics, though it is incremental as it automates an existing process rather than introducing a new paradigm.
The paper tackles the labor-intensive manual construction of semantic map models (SMMs) by proposing a novel graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner, demonstrating effectiveness and efficiency in a case study on cross-linguistic supplementary adverbs.
Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms, based on the connectivity hypothesis. This approach has been widely used to represent similarity and entailment relationships in cross-linguistic concept comparisons. However, most SMMs are manually built by human experts using bottom-up procedures, which are often labor-intensive and time-consuming. In this paper, we propose a novel graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner. The algorithm begins by creating a dense graph, which is subsequently pruned into maximum spanning trees, selected according to metrics we propose. These evaluation metrics include both intrinsic and extrinsic measures, considering factors such as network structure and the trade-off between precision and coverage. A case study on cross-linguistic supplementary adverbs demonstrates the effectiveness and efficiency of our model compared to human annotations and other automated methods. The tool is available at https://github.com/RyanLiut/SemanticMapModel.