We can still parse using syntactic rules

• URL: http://arxiv.org/abs/2602.14238v1
• Date: Sun, 15 Feb 2026 17:16:32 GMT
• Title: We can still parse using syntactic rules
• Authors: Ghaly Hussein,
• Abstract summary: This research introduces a new parsing approach, based on earlier syntactic work on context free grammar (CFG) and generalized phrase structure grammar (GPSG)<n>The approach comprises both a new parsing algorithm and a set of syntactic rules and features that overcome the limitations of CFG.<n>It also generates both dependency and constituency parse trees, while accommodating noise and incomplete parses.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Deep networks learn to parse uniform-depth context-free languages from local statistics [12.183764229746926]
  Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning.<n>We introduce a class of context-free grammars (PCFGs) in which both the degree of ambiguity and the correlation structure across scales can be controlled.<n>We propose a unifying framework where correlations at different scales lift local ambiguities, enabling the emergence of hierarchical representations of the data.
  arXiv  Detail & Related papers  (2026-01-31T17:35:06Z)
• Syntactic Control of Language Models by Posterior Inference [53.823006836309695]
  Controlling the syntactic structure of text generated by language models is valuable for applications requiring clarity, stylistic consistency, or interpretability.<n>We argue that sampling algorithms based on the posterior inference can effectively enforce a target constituency structure during generation.<n>Our approach combines sequential Monte Carlo, which estimates the posterior distribution by sampling from a proposal distribution, with a syntactic tagger that ensures that each generated token aligns with the desired syntactic structure.
  arXiv  Detail & Related papers  (2025-06-08T14:01:34Z)
• EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checking [58.15568681219339]
  We introduce EquiBench, a new benchmark for evaluating large language models (LLMs)<n>This task directly tests a model's ability to reason about program semantics.<n>We evaluate 19 state-of-the-art LLMs and find that in the most challenging categories, the best accuracies are 63.8% and 76.2%, only modestly above the 50% random baseline.
  arXiv  Detail & Related papers  (2025-02-18T02:54:25Z)
• Improving Unsupervised Constituency Parsing via Maximizing Semantic Information [35.63321102040579]
  Unsupervised constituencys organize phrases within a sentence into a tree-shaped syntactic constituent structure.<n>Traditional objective of maximizing sentence log-likelihood does not explicitly account for the close relationship between the constituent structure and the semantics.<n>We introduce a novel objective that trains parsings by maximizing SemInfo, the semantic information encoded in constituent structures.
  arXiv  Detail & Related papers  (2024-10-03T15:04:00Z)
• Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech [8.550564152063522]
  We report on a set of experiments aiming at assessing the performance of two parsing paradigms on speech parsing. We perform this evaluation on a large treebank of spoken French, featuring realistic spontaneous conversations. Our findings show that (i) the graph based approach obtains better results across the board (ii) parsing directly from speech outperforms a pipeline approach, despite having 30% fewer parameters.
  arXiv  Detail & Related papers  (2024-06-18T13:46:10Z)
• Cross-domain Chinese Sentence Pattern Parsing [67.1381983012038]
  Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching. Existing SPSs rely heavily on textbook corpora for training, lacking cross-domain capability. This paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework.
  arXiv  Detail & Related papers  (2024-02-26T05:30:48Z)
• Compositional Generalization for Data-to-Text Generation [86.79706513098104]
  We propose a novel model that addresses compositional generalization by clustering predicates into groups. Our model generates text in a sentence-by-sentence manner, relying on one cluster of predicates at a time. It significantly outperforms T5baselines across all evaluation metrics.
  arXiv  Detail & Related papers  (2023-12-05T13:23:15Z)
• Compositional Program Generation for Few-Shot Systematic Generalization [59.57656559816271]
  This study on a neuro-symbolic architecture called the Compositional Program Generator (CPG) CPG has three key features: textitmodularity, textitcomposition, and textitabstraction, in the form of grammar rules. It perfect achieves generalization on both the SCAN and COGS benchmarks using just 14 examples for SCAN and 22 examples for COGS.
  arXiv  Detail & Related papers  (2023-09-28T14:33:20Z)
• Unsupervised Chunking with Hierarchical RNN [62.15060807493364]
  This paper introduces an unsupervised approach to chunking, a syntactic task that involves grouping words in a non-hierarchical manner. We present a two-layer Hierarchical Recurrent Neural Network (HRNN) designed to model word-to-chunk and chunk-to-sentence compositions. Experiments on the CoNLL-2000 dataset reveal a notable improvement over existing unsupervised methods, enhancing phrase F1 score by up to 6 percentage points.
  arXiv  Detail & Related papers  (2023-09-10T02:55:12Z)
• The Limitations of Limited Context for Constituency Parsing [27.271792317099045]
  Parsing-Reading-Predict architecture of (Shen et al., 2018a) was first to perform unsupervised syntactic parsing. What kind of syntactic structure can current neural approaches to syntax represent? We ground this question in the sandbox of probabilistic-free-grammars (PCFGs) We identify a key aspect of the representational power of these approaches: the amount and directionality of context that the predictor has access to.
  arXiv  Detail & Related papers  (2021-06-03T03:58:35Z)
• Iterative Utterance Segmentation for Neural Semantic Parsing [38.344720207846905]
  We present a novel framework for boosting neural semantic domains via iterative utterance segmentation. One key advantage is that this framework does not require any handcraft utterance or additional labeled data for the segmenter. On data that require compositional generalization, our framework brings significant accuracy: Geo 63.1 to 81.2, Formulas 59.7 to 72.7, ComplexWebQuestions 27.1 to 56.3.
  arXiv  Detail & Related papers  (2020-12-13T09:46:24Z)
• Infusing Finetuning with Semantic Dependencies [62.37697048781823]
  We show that, unlike syntax, semantics is not brought to the surface by today's pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning.
  arXiv  Detail & Related papers  (2020-12-10T01:27:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.