Computationalwhose

Computationalwhose is a concept at the intersection of computational linguistics, formal semantics, and natural language understanding that focuses on the representation, interpretation, and processing of possessive relative constructions, especially the word whose. The term refers to methods and models that capture ownership relations expressed by possessive pronouns within sentences and across discourse.

Overview and scope

Computationalwhose addresses how ownership is encoded in grammar, how possessive dependencies are resolved across clauses, and how ownership information contributes to truth-conditions and inference. It encompasses lexical semantics of possessives, the syntax-semantics interface for relative clauses, and cross-sentential anaphora involving ownership. The field aims to make systems robust to ambiguity in who owns what, and to enable precise reasoning about possession in complex sentences.

Methods and representations

Approaches combine rule-based and data-driven techniques. Formal semantic representations often employ lambda calculus or first-order logic with ownership predicates. Dynamic and discourse-level models track ownership as information evolves across utterances. Graph-based representations, such as semantic networks, are used to encode possession relations. Neural models are trained on ownership-annotated corpora and integrated with structural constraints to improve interpretability. Evaluation typically involves parsing accuracy for possession relations, correct antecedent resolution, and generation or translation quality that preserves ownership semantics.

Applications and challenges

Applications include intelligent assistants, information extraction, question answering, and knowledge graphs where accurate possession relations improve retrieval and reasoning. Challenges include ambiguity in antecedent choice, cases of multiple owners, cross-linguistic variation in possessive marking, and limited annotated data. Ongoing work seeks to unify formal semantics with scalable neural methods and to extend cross-linguistic coverage.

See also

Possessive pronouns, relative clauses, anaphora resolution, dynamic semantics, ownership modeling in AI.