SOTAVerified

Constituency Parsing

Constituency parsing aims to extract a constituency-based parse tree from a sentence that represents its syntactic structure according to a phrase structure grammar.

Example:

             Sentence (S)
                 |
   +-------------+------------+
   |                          |
 Noun (N)                Verb Phrase (VP)
   |                          |
 John                 +-------+--------+
                      |                |
                    Verb (V)         Noun (N)
                      |                |
                    sees              Bill

Recent approaches convert the parse tree into a sequence following a depth-first traversal in order to be able to apply sequence-to-sequence models to it. The linearized version of the above parse tree looks as follows: (S (N) (VP V N)).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 204 papers

TitleStatusHype
DepNeCTI: Dependency-based Nested Compound Type Identification for SanskritCode0
BERT-Proof Syntactic Structures: Investigating Errors in Discontinuous Constituency ParsingCode0
Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic OraclesCode0
To be Continuous, or to be Discrete, Those are Bits of QuestionsCode0
Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar InductionCode0
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic OracleCode0
Parsing with Traces: An O(n^4) Algorithm and a Structural RepresentationCode0
Parsing as Language ModelingCode0
Probabilistic Graph-based Dependency Parsing with Convolutional Neural NetworkCode0
Assessing the Use of Prosody in Constituency Parsing of Imperfect TranscriptsCode0
Neural Combinatory Constituency ParsingCode0
Should Have, Would Have, Could Have. Investigating Verb Group Representations for Parsing with Universal Dependencies.Code0
PTB Graph Parsing with Tree ApproximationCode0
Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language ModelsCode0
Approximating CKY with TransformersCode0
Ensemble Distillation for Unsupervised Constituency ParsingCode0
LLM-enhanced Self-training for Cross-domain Constituency ParsingCode0
Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence GenerationCode0
Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency ParsingCode0
Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary TasksCode0
Learning Syntax from Naturally-Occurring BracketingsCode0
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?Code0
Effective Self-Training for ParsingCode0
Less Grammar, More FeaturesCode0
Recurrent Neural Network GrammarsCode0
Gaussian Mixture Latent Vector GrammarsCode0
Generalizing Natural Language Analysis through Span-relation RepresentationsCode0
Converting the Point of View of Messages Spoken to Virtual AssistantsCode0
Rethinking Self-Attention: Towards Interpretability in Neural ParsingCode0
Grammar as a Foreign LanguageCode0
Grammar Induction with Neural Language Models: An Unusual ReplicationCode0
Effective Inference for Generative Neural Parsing—0
Do Transformers Parse while Predicting the Masked Word?—0
Domain Adaptation for Dependency Parsing via Self-Training—0
Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction—0
Discriminative Strategies to Integrate Multiword Expression Recognition and Parsing—0
Discosuite - A parser test suite for German discontinuous structures—0
Discontinuity (Re) ^2-visited: A Minimalist Approach to Pseudoprojective Constituent Parsing—0
Boosting for Efficient Model Selection for Syntactic Parsing—0
An Empirical Study for Vietnamese Constituency Parsing with Pre-training—0
A Generative Parser with a Discriminative Recognition Algorithm—0
DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment—0
Bilingually-Guided Monolingual Dependency Grammar Induction—0
Dialog Generation Using Multi-Turn Reasoning Neural Networks—0
An Empirical Investigation of Statistical Significance in NLP—0
Dependency Parser Adaptation with Subtrees from Auto-Parsed Target Domain Data—0
Iterative Transformation of Annotation Guidelines for Constituency Parsing—0
A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models—0
Investigating NP-Chunking with Universal Dependencies for English—0
Investigating Non-local Features for Neural Constituency Parsing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Hashing + XLNetF1 score96.43—Unverified
2SAPar + XLNetF1 score96.4—Unverified
3Label Attention Layer + HPSG + XLNetF1 score96.38—Unverified
4Attach-Juxtapose Parser + XLNetF1 score96.34—Unverified
5Head-Driven Phrase Structure Grammar Parsing (Joint) + XLNetF1 score96.33—Unverified
6CRF Parser + RoBERTaF1 score96.32—Unverified
7Hashing + BertF1 score96.03—Unverified
8NFC + BERT-largeF1 score95.92—Unverified
9N-ary semi-markov + BERT-largeF1 score95.92—Unverified
10Head-Driven Phrase Structure Grammar Parsing (Joint) + BERTF1 score95.84—Unverified
#ModelMetricClaimedVerifiedStatus
1Attach-Juxtapose Parser + BERTF1 score93.52—Unverified
2SAPar + BERTF1 score92.66—Unverified
3N-ary semi-markov + BERTF1 score92.5—Unverified
4Hashing + BertF1 score92.33—Unverified
5CRF Parser + BERTF1 score92.27—Unverified
6Kitaev etal. 2019F1 score91.75—Unverified
7CRF ParserF1 score89.8—Unverified
8Zhou etal. 2019F1 score89.4—Unverified
9Kitaev etal. 2018F1 score87.43—Unverified
#ModelMetricClaimedVerifiedStatus
1CRF Parser + ElectraF1 score91.92—Unverified
2CRF Parser + BERTF1 score91.55—Unverified
3CRF ParserF1 score88.6—Unverified
#ModelMetricClaimedVerifiedStatus
1SAParF183.26—Unverified