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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 61–70 of 204 papers

Title	Date	Tasks	Status	Score
Investigating Non-local Features for Neural Constituency Parsing	Sep 27, 2021	Constituency Parsing	CodeCode Available	5
Head-Driven Phrase Structure Grammar Parsing on Penn Treebank	Jul 5, 2019	Constituency ParsingDecoder	CodeCode Available	5
Ensemble Distillation for Unsupervised Constituency Parsing	Oct 3, 2023	Constituency Grammar InductionConstituency Parsing	CodeCode Available	5
Grammar as a Foreign Language	Dec 23, 2014	Constituency ParsingCPU	CodeCode Available	5
Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency Parsing	Feb 29, 2024	AllConstituency Parsing	CodeCode Available	5
Gaussian Mixture Latent Vector Grammars	May 12, 2018	Constituency ParsingPart-Of-Speech Tagging	CodeCode Available	5
Effective Self-Training for Parsing	Jun 1, 2006	Constituency Parsing	CodeCode Available	5
Approximating CKY with Transformers	May 3, 2023	Constituency ParsingInductive Bias	CodeCode Available	5
Grammar Induction with Neural Language Models: An Unusual Replication	Aug 29, 2018	Constituency ParsingLanguage Modeling	CodeCode Available	5
Generalizing Natural Language Analysis through Span-relation Representations	Nov 10, 2019	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	5

Show:10 25 50

← PrevPage 7 of 21Next →

All datasets Penn Treebank CTB5 CTB7 ATB

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Hashing + XLNet	F1 score	96.43	—	Unverified
2	SAPar + XLNet	F1 score	96.4	—	Unverified
3	Label Attention Layer + HPSG + XLNet	F1 score	96.38	—	Unverified
4	Attach-Juxtapose Parser + XLNet	F1 score	96.34	—	Unverified
5	Head-Driven Phrase Structure Grammar Parsing (Joint) + XLNet	F1 score	96.33	—	Unverified
6	CRF Parser + RoBERTa	F1 score	96.32	—	Unverified
7	Hashing + Bert	F1 score	96.03	—	Unverified
8	NFC + BERT-large	F1 score	95.92	—	Unverified
9	N-ary semi-markov + BERT-large	F1 score	95.92	—	Unverified
10	Head-Driven Phrase Structure Grammar Parsing (Joint) + BERT	F1 score	95.84	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Attach-Juxtapose Parser + BERT	F1 score	93.52	—	Unverified
2	SAPar + BERT	F1 score	92.66	—	Unverified
3	N-ary semi-markov + BERT	F1 score	92.5	—	Unverified
4	Hashing + Bert	F1 score	92.33	—	Unverified
5	CRF Parser + BERT	F1 score	92.27	—	Unverified
6	Kitaev etal. 2019	F1 score	91.75	—	Unverified
7	CRF Parser	F1 score	89.8	—	Unverified
8	Zhou etal. 2019	F1 score	89.4	—	Unverified
9	Kitaev etal. 2018	F1 score	87.43	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CRF Parser + Electra	F1 score	91.92	—	Unverified
2	CRF Parser + BERT	F1 score	91.55	—	Unverified
3	CRF Parser	F1 score	88.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SAPar	F1	83.26	—	Unverified