A Method for Learning Large-Scale Computational Construction Grammars from Semantically Annotated Corpora
arXiv:2603.12754v1 Announce Type: new Abstract: We present a method for learning large-scale, broad-coverage construction grammars from corpora of language use. Starting from utterances annotated with constituency structure and semantic frames, the method facilitates the learning of human-interpretable computational construction grammars that capture the intricate relationship between syntactic structures and the semantic relations they express. The resulting grammars consist of networks of tens of thousands of constructions formalised within the Fluid Construction Grammar framework. Not only do these grammars support the frame-semantic analysis of open-domain text, they also house a trove of information about the syntactico-semantic usage patterns present in the data they were learnt from. The method and learnt grammars contribute to the scaling of usage-based, constructionist approaches to language, as they corroborate the scalability of a number of fundamental construction grammar
arXiv:2603.12754v1 Announce Type: new Abstract: We present a method for learning large-scale, broad-coverage construction grammars from corpora of language use. Starting from utterances annotated with constituency structure and semantic frames, the method facilitates the learning of human-interpretable computational construction grammars that capture the intricate relationship between syntactic structures and the semantic relations they express. The resulting grammars consist of networks of tens of thousands of constructions formalised within the Fluid Construction Grammar framework. Not only do these grammars support the frame-semantic analysis of open-domain text, they also house a trove of information about the syntactico-semantic usage patterns present in the data they were learnt from. The method and learnt grammars contribute to the scaling of usage-based, constructionist approaches to language, as they corroborate the scalability of a number of fundamental construction grammar conjectures while also providing a practical instrument for the constructionist study of English argument structure in broad-coverage corpora.
Executive Summary
This article presents a novel method for learning large-scale computational construction grammars from semantically annotated corpora. The approach enables the learning of human-interpretable grammars that capture the complex relationship between syntactic structures and semantic relations. The resulting grammars, formalized within the Fluid Construction Grammar framework, support frame-semantic analysis of open-domain text and provide valuable insights into syntactico-semantic usage patterns. The method contributes to the scaling of usage-based, constructionist approaches to language, corroborating fundamental construction grammar conjectures and offering a practical tool for studying English argument structure.
Key Points
- ▸ Learning large-scale computational construction grammars from corpora
- ▸ Capturing the relationship between syntactic structures and semantic relations
- ▸ Applicability to open-domain text analysis and syntactico-semantic usage pattern study
Merits
Scalability
The method enables the learning of large-scale grammars, making it a valuable tool for constructionist approaches to language.
Interpretability
The resulting grammars are human-interpretable, allowing for a deeper understanding of the complex relationships between syntactic structures and semantic relations.
Demerits
Data Quality Dependence
The method's effectiveness relies heavily on the quality and accuracy of the semantically annotated corpora used for training.
Computational Complexity
The learning process may be computationally intensive, requiring significant resources and potentially limiting its applicability to large-scale datasets.
Expert Commentary
The proposed method represents a significant advancement in the field of construction grammar, offering a scalable and interpretable approach to learning large-scale computational grammars. The ability to capture the intricate relationships between syntactic structures and semantic relations has far-reaching implications for NLP and linguistic theory. However, the method's dependence on high-quality training data and potential computational complexity must be carefully considered in future applications. As the field continues to evolve, it is essential to address these challenges and explore the method's potential in various domains.
Recommendations
- ✓ Further research into the method's applicability to diverse languages and domains
- ✓ Development of more efficient and scalable algorithms for learning large-scale construction grammars