We can still parse using syntactic rules
It provides a transparent and interpretable NLP model for language processing, but appears incremental as it builds on earlier syntactic work.
This research tackles the problem of parsing natural language by introducing a new approach based on syntactic rules, which generates dependency and constituency parse trees while handling noise and incomplete parses, achieving an average Unlabeled Attachment Score of 54.5% on development data and 53.8% on test data.
This research introduces a new parsing approach, based on earlier syntactic work on context free grammar (CFG) and generalized phrase structure grammar (GPSG). The approach comprises both a new parsing algorithm and a set of syntactic rules and features that overcome the limitations of CFG. It also generates both dependency and constituency parse trees, while accommodating noise and incomplete parses. The system was tested on data from Universal Dependencies, showing a promising average Unlabeled Attachment Score (UAS) of 54.5% in the development dataset (7 corpora) and 53.8% in the test set (12 corpora). The system also provides multiple parse hypotheses, allowing further reranking to improve parsing accuracy. This approach also leverages much of the theoretical syntactic work since the 1950s to be used within a computational context. The application of this approach provides a transparent and interpretable NLP model to process language input.