Large-scale multimodal communication research increasingly depends on computational methods capable of extracting reliable gesture annotations from video data. Yet it remains unclear to what extent automatically generated annotations can support the linguistic investigation of naturally occurring co-speech gesture and where expert interpretation remains indispensable. This paper presents a framework for the automatic analysis and annotation of co-speech gesture in media corpora, developed to support multimodal communication research at scale. We build our framework on top of the existing computer vision frameworks for keypoint extraction and active speaker detection, with post-processing steps carefully designed to mitigate noisy output. Building on the processed keypoint trajectories, we implement a configurable rule-based annotation system developed through close collaboration between computer vision researchers and linguists. We demonstrate the usefulness of the framework through a case study investigating vertical hand movement accompanying English expressions of future and past, examining which aspects of this gestural pattern can be captured through automatic annotation and which require expert multimodal interpretation. We find that at corpus scale, greater mean amplitude and higher mean peak velocity converge in *will*-future, suggesting greater kinematic prominence in the multimodal expression of futurity. More broadly, the case study demonstrates how computational pipelines can support, rather than replace, human analysis by enabling multimodal hypotheses to be investigated on datasets that would be impractical to analyse manually.

This paper introduces the Research Constructicon (RCxn), a project developed within the Research Training Group Dimensions of Constructional Space. The training group finances PhD projects in the framework of Construction Grammar (CxG), which views language as a network of form-meaning pairings. The RCxn is designed as a dynamic, community-driven resource that documents linguistic constructions while also capturing the research processes and findings associated with them. The project addresses three core dimensions: (1) the development of a modular ontology to represent constructions, their relationships, and the research surrounding them; (2) the implementation of database populated by researchers’ contributions; and (3) the creation of a web application to visualize and interact with the data. This paper focuses on our work to implement a rich ontology for the RCxn, which has to accommodate diverse research needs, from cross-linguistic comparisons to multimodal analyses, while ensuring flexibility and interoperability. We detail the modular design of the ontology, its alignment with semantic web standards (RDF/OWL), and the integration of existing ontologies (e.g., OLiA, FOAF). The RCxn’s development is iterative, driven by feedback from our diverse group of PhD researchers.

This paper presents MoCCA, a Model of Comparative Concepts for Aligning Constructicons under development by a consortium of research groups building Constructicons of different languages including Brazilian Portuguese, English, German and Swedish. The Constructicons will be aligned by using comparative concepts (CCs) providing language-neutral definitions of linguistic properties. The CCs are drawn from typological research on grammatical categories and constructions, and from FrameNet frames, organized in a conceptual network. Language-specific constructions are linked to the CCs in accordance with general principles. MoCCA is organized into files of two types: a largely static CC Database file and multiple Linking files containing relations between constructions in a Constructicon and the CCs. Tools are planned to facilitate visualization of the CC network and linking of constructions to the CCs. All files and guidelines will be versioned, and a mechanism is set up to report cases where a language-specific construction cannot be easily linked to existing CCs.

State-of-the-art dependency representations such as the Stanford Typed Dependencies may represent the grammatical relations in a sentence as directed, possibly cyclic graphs. Querying a syntactically annotated corpus for grammatical structures that are represented as graphs requires graph matching, which is a non-trivial task. In this paper, we present an algorithm for graph matching that is tailored to the properties of large, syntactically annotated corpora. The implementation of the algorithm is built on top of the popular IMS Open Corpus Workbench, allowing corpus linguists to re-use existing infrastructure. An evaluation of the resulting software, CWB-treebank, shows that its performance in real world applications, such as a web query interface, compares favourably to implementations that rely on a relational database or a dedicated graph database while at the same time offering a greater expressive power for queries. An intuitive graphical interface for building the query graphs is available via the Treebank.info project.