Improving Shift‐Reduce Phrase‐Structure Parsing with Constituent Boundary Information
Bibliographic record
Abstract
Shift‐reduce parsing enjoys the property of efficiency because of the use of efficient parsing algorithms like greedy/deterministic search and beam search. In addition, shift‐reduce parsing is much simpler and easy to implement compared with other parsing algorithms. In this article, we explore constituent boundary information to improve the performance of shift‐reduce phrase‐structure parsing. In previous work, constituent boundary information has been used to speed up chart parsers successfully. However, whether it is useful for improving parsing accuracy has not been investigated. We propose two different models to capture constituent boundary information, based on which two sets of novel features are designed for a shift‐reduce parser. The first model is a boundary prediction model that uses a classifier to predict the boundaries of constituents. We use automatically parsed data to train the classifier. The second one is a Tree Likelihood Model that measures the validity of a constituent by its likelihood which is calculated on automatically parsed data. Experimental results show that our proposed method outperforms a strong baseline by 0.8%and 1.6%in F‐score on English and Chinese data, respectively, achieving the competitive parsing accuracies on Chinese (84.8%) and English (90.8%). To our knowledge, this is the first time for shift‐reduce phrase‐structure parsing to advance the state‐of‐the‐art with constituent boundary information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".