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 141–150 of 204 papers

Title	Date	Tasks	Status
Gradient-based Inference for Networks with Output Constraints	Jul 26, 2017	Constituency ParsingSemantic Role Labeling	—Unverified
Parsing with Traces: An O(n^4) Algorithm and a Structural Representation	Jul 13, 2017	Constituency ParsingMissing Elements	CodeCode Available
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects	Jul 10, 2017	Constituency ParsingReranking	—Unverified
YellowFin and the Art of Momentum Tuning	Jun 12, 2017	Constituency ParsingLanguage Modeling	CodeCode Available
A Minimal Span-Based Neural Constituency Parser	May 10, 2017	Constituency Parsing	—Unverified
Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks	Apr 1, 2017	Constituency ParsingMorphological Analysis	CodeCode Available
Temporal@ODIL project: Adapting ISO-TimeML to syntactic treebanks for the temporal annotation of spoken speech	Jan 1, 2017	Constituency Parsing	—Unverified
Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing	Jan 1, 2017	Constituency ParsingDecision Making	—Unverified
Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles	Dec 20, 2016	Constituency ParsingDependency Parsing	CodeCode Available
Improving Neural Translation Models with Linguistic Factors	Dec 1, 2016	Constituency ParsingDependency Parsing	—Unverified

Show:10 25 50

← PrevPage 15 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	N-ary semi-markov + BERT-large	F1 score	95.92	—	Unverified
9	NFC + 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