Abstract

Large language models (llms) have made language technologies widely accessible to humanities scholars, but they have also intensified concerns about transparency, reproducibility, and interpretive responsibility. This article argues that graph-based meaning representations, especially Abstract Meaning Representation (amr) and Uniform Meaning Representation (umr), can function as interpretive infrastructure for humanities research in the age of llms. Meaning graphs are “thin” by design in that they deliberately encode a constrained set of semantic distinctions. That selectivity is not a weakness for humanistic inquiry; rather, it enables a disciplined workflow in which researchers can separate (i) the semantic commitments that a text licenses (events, participants, temporal and modal dependencies) from (ii) richer interpretive claims (stance, ideology, affect, narrative framing) that can be layered on top. I review amr and umr at a level accessible to humanities audiences, discuss what changes in the llm era (including both opportunities and limits of using llms for semantic parsing), and propose humanities-centered workflows and research questions. Several compact sample analyses illustrate how meaning graphs can support interpretive tasks in historiography, narrative analysis, and translation studies. A final section explicitly lists resources (datasets, tools, and guidelines) to support reproducible experimentation.

• Keywords: Abstract Meaning Representation (amr), Uniform Meaning Representation (umr), meaning graphs, digital humanities, large language models