A computational model for the item-based induction of construction networks

Area of Science:

• Computational Linguistics
• Cognitive Science
• Language Acquisition

Background:

• Usage-based theories propose linguistic knowledge comprises constructions (form-meaning pairings) at various abstraction levels.
• Syntactic knowledge is thought to emerge from abstracting lexically specific knowledge.
• Existing computational models often focus on word-referent mappings, limiting scope.

Purpose of the Study:

• To computationally model the gradual emergence of a network of constructions.
• To explore how cross-situational learning can extend beyond word-referent mappings.
• To investigate the abstraction of complex form-meaning correspondences.

Main Methods:

• Developed a computational model simulating language acquisition through observing natural language utterances.
• Utilized the principle of cross-situational learning to infer meanings from ambiguous contexts.
• Applied cross-situational learning to learn complex form-meaning correspondences.

Main Results:

• Successfully modeled the emergence of a network of constructions with varying complexity.
• Demonstrated the applicability of cross-situational learning for abstract form-meaning pairings.
• Showed a computational pathway for learning beyond simple word-referent associations.

Conclusions:

• Cross-situational learning is a viable mechanism for acquiring complex linguistic constructions.
• Computational modeling can elucidate the gradual abstraction process in language acquisition.
• The model provides a framework for understanding how abstract syntactic knowledge emerges.