Discourse Markers and Interactional Structure
How pragmatic markers, recurrent bundles, and discourse routines organize turns, authority, alignment, and narrative progression in interaction.
LOPE develops research programs at the intersection of lexical semantics, discourse analysis, and computational linguistics. We build knowledge resources (from Chinese Wordnet to historical corpus platforms) and pursue data-driven inquiries into how language encodes meaning, stance, and cultural knowledge.
Area 01
Research on how language encodes stance, evaluation, and social meaning: from face-to-face interaction and discourse structure to large-scale patterns in social media and online public discourse.
This area examines how speakers use lexical choices, recurrent expressions, and culturally grounded categories to take stances, coordinate action, and produce social meaning. The central question is not only what words mean in the abstract, but how meaning is produced, negotiated, and contested across different communicative contexts.
LOPE's work here spans qualitative discourse analysis and corpus-scale computational methods. A significant body of research addresses Chinese social media, public opinion, and sentiment, examining how stance, affect, and evaluation circulate on digital platforms. This connects to broader questions about how language indexes identity, authority, and social alignment.
How pragmatic markers, recurrent bundles, and discourse routines organize turns, authority, alignment, and narrative progression in interaction.
Computational and corpus-based approaches to detecting evaluative language, opinion, and affect, with a focus on Chinese text and social media discourse.
How lexical choices encode situated cultural knowledge, local interpretive norms, and socially legible categories of action across Chinese and cross-linguistic data.
Area 02
Projects that build and test structured language resources, cognitive models, and data-driven methods to support robust theories of lexical organization, morphology, and cognitive representation.
This research area brings together lexical semantics, ontology design, morphological analysis, and computational modeling. Rather than treating language technology as a detached engineering problem, the work uses formal and computational methods to probe what constitutes a meaningful representation of language and thought.
A central contribution is the Chinese Wordnet and associated ontological infrastructure: multilingual lexical resources that connect word senses, semantic relations, and conceptual hierarchies across languages. The lab also pursues cognitive and neural investigations, using behavioral experiments, ERP, and fMRI methods to examine how the mind organizes lexical and morphological knowledge.
Building and extending interoperable lexical resources that connect synsets, semantic relations, and ontology-backed representations, with Chinese Wordnet as the lab's flagship contribution.
Analyzing affixation, compounding, and word-formation processes in Chinese and cross-linguistically, connecting corpus evidence to formal morphological theory.
Using behavioral, ERP, and fMRI methods to examine how lexical, morphological, and semantic structures are represented and processed in the mind and brain.
Area 03
Infrastructure and methodological work that allows humanities research to engage computation without losing disciplinary specificity, interpretive accountability, or cultural nuance.
LOPE approaches digital humanities as an infrastructural and methodological challenge. The work includes building search systems, annotation frameworks, and corpus platforms for historical and contemporary materials, including Chinese Buddhist texts, classical literary corpora, and cultural heritage collections.
This work foregrounds pluralism by design. Systems should remain legible to scholars with different methods, domains, and evidentiary traditions, and they should support exploration without collapsing historical or cultural materials into a flattened computational frame. The lab's platforms are built to serve both machine-readable precision and humanistic interpretive depth.
Developing corpora, annotation schemes, and retrieval systems for historical Chinese texts, including Buddhist, classical, and premodern literary materials.
Building search interfaces that move beyond exact keyword matching while remaining transparent about how retrieval and interpretation are being shaped.
Developing tools and workflows that match real scholarly practice, from ontology-backed browsing to interpretability features that make system reasoning legible to researchers.