RELlukemat

RELlukemat is a theoretical framework used in computational linguistics and semiotics to model relational semantics among lexical items through a family of matrices known as RELlukemat matrices. The concept aims to capture relationships between lemmas across multiple axes such as synonymy, hypernymy, co-occurrence, topical association, and functional roles, providing a structured representation beyond simple co-occurrence statistics.

The formal structure centers on a base set of lemmas L and a set of relation types R. For each relation type r in R, there is a corresponding matrix M_r where the entry M_r[i, j] represents the strength or significance of relation r between lemmas i and j. Matrices can be weighted, normalized, and combined to form composite representations or embedded into higher-order tensors to reflect interactions among several relation types. The framework supports sense-level granularity by indexing lemmas with sense inventories or disambiguated representations.

Computationally, RELlukemat can be learned from large text corpora through factorization techniques, co-occurrence statistics, and contextual embeddings, or assembled from curated lexical resources. The per-entry specification of relation types aids interpretability, allowing targeted queries such as identifying terms related by a specific relation or discovering terms that participate in a given semantic pattern. RELlukemat representations can feed downstream tasks in natural language processing, including semantic search, information retrieval, word-sense disambiguation, and knowledge-graph completion.

History and status notes describe RELlukemat as a developing approach in theoretical and experimental linguistics. Ongoing work focuses on scalability to large vocabularies, multilingual applicability, and robust alignment with established lexical resources. Related concepts include semantic networks, knowledge graphs, and various matrix- and tensor-based embedding methods.