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 126–150 of 204 papers

Title	Date	Tasks	Status	Hype
Dialog Generation Using Multi-Turn Reasoning Neural Networks	Jun 1, 2018	Constituency ParsingImage Captioning	—Unverified	0
A Dependency Perspective on RST Discourse Parsing and Evaluation	Jun 1, 2018	Constituency ParsingDependency Parsing	—Unverified	0
Linear-Time Constituency Parsing with RNNs and Dynamic Programming	May 17, 2018	Boundary DetectionConstituency Parsing	—Unverified	0
Gaussian Mixture Latent Vector Grammars	May 12, 2018	Constituency ParsingPart-Of-Speech Tagging	CodeCode Available	0
Constituency Parsing with a Self-Attentive Encoder	May 2, 2018	Constituency ParsingSentence	CodeCode Available	1
A New Version of the Sk Treebank of Polish Harmonised with the Walenty Valency Dictionary	May 1, 2018	Constituency Parsing	—Unverified	0
Coreference Resolution in FreeLing 4.0	May 1, 2018	Constituency Parsingcoreference-resolution	—Unverified	0
What's Going On in Neural Constituency Parsers? An Analysis	Apr 20, 2018	Constituency Parsing	CodeCode Available	0
Attentive Tensor Product Learning	Feb 20, 2018	Constituency ParsingDeep Learning	—Unverified	0
Supervised Attention for Sequence-to-Sequence Constituency Parsing	Nov 1, 2017	Constituency ParsingMachine Translation	—Unverified	0
Optimizing for Measure of Performance in Max-Margin Parsing	Sep 5, 2017	Constituency ParsingStructured Prediction	—Unverified	0
Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages	Sep 1, 2017	Constituency ParsingDiversity	—Unverified	0
Neural Discontinuous Constituency Parsing	Sep 1, 2017	Constituency Parsing	—Unverified	0
A Generative Parser with a Discriminative Recognition Algorithm	Aug 1, 2017	Constituency ParsingDecoder	—Unverified	0
Effective Inference for Generative Neural Parsing	Jul 27, 2017	Constituency ParsingReranking	—Unverified	0
Gradient-based Inference for Networks with Output Constraints	Jul 26, 2017	Constituency ParsingSemantic Role Labeling	—Unverified	0
Parsing with Traces: An O(n^4) Algorithm and a Structural Representation	Jul 13, 2017	Constituency ParsingMissing Elements	CodeCode Available	0
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects	Jul 10, 2017	Constituency ParsingReranking	—Unverified	0
YellowFin and the Art of Momentum Tuning	Jun 12, 2017	Constituency ParsingLanguage Modeling	CodeCode Available	0
A Minimal Span-Based Neural Constituency Parser	May 10, 2017	Constituency Parsing	—Unverified	0
Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks	Apr 1, 2017	Constituency ParsingMorphological Analysis	CodeCode Available	0
Temporal@ODIL project: Adapting ISO-TimeML to syntactic treebanks for the temporal annotation of spoken speech	Jan 1, 2017	Constituency Parsing	—Unverified	0
Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing	Jan 1, 2017	Constituency ParsingDecision Making	—Unverified	0
Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles	Dec 20, 2016	Constituency ParsingDependency Parsing	CodeCode Available	0
Improving Neural Translation Models with Linguistic Factors	Dec 1, 2016	Constituency ParsingDependency Parsing	—Unverified	0

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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