We employ probabilistic decision trees combined with fine-tuning techniques to address the challenging distinction between noun complement clauses and relative clauses. The approach includes careful annotation and evaluation protocols to ensure accurate classification of these subtle grammatical structures.
The methodology involves creating a dataset of carefully annotated examples, training probabilistic decision trees on linguistic features, and fine-tuning the models to improve performance on this specific grammatical distinction. We evaluate the approach using standard parsing metrics and compare it with existing methods.
The distinction between noun complement clauses and relative clauses with postnominal 'that' is particularly challenging because:
Our probabilistic decision tree approach achieves significant improvements over baseline methods in distinguishing between noun complement clauses and relative clauses. The fine-tuning process allows the model to learn subtle linguistic patterns that are difficult to capture with traditional approaches.
This work contributes to computational linguistics by developing methods for handling subtle grammatical distinctions. The findings have implications for improving parsing accuracy and linguistic understanding in NLP systems, particularly for applications that require precise grammatical analysis.
The probabilistic decision tree approach can be extended to other challenging grammatical phenomena, providing a framework for addressing similar linguistic challenges in different languages and contexts.