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
Assigning Deep Lexical Types Using Structured Classifier Features for Grammatical Dependencies	Jul 1, 2012	Constituency ParsingLanguage Modelling	—Unverified
Effective Inference for Generative Neural Parsing	Jul 27, 2017	Constituency ParsingReranking	—Unverified
Construction of an English Dependency Corpus incorporating Compound Function Words	May 1, 2016	Constituency ParsingDependency Parsing	—Unverified
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications	Nov 1, 2019	Constituency ParsingRelation	—Unverified
Unsupervised Parsing by Searching for Frequent Word Sequences among Sentences with Equivalent Predicate-Argument Structures	Apr 18, 2024	Constituency ParsingSentence	—Unverified
A Multi-Teraflop Constituency Parser using GPUs	Oct 1, 2013	Constituency Parsing	—Unverified
Fast Unsupervised Dependency Parsing with Arc-Standard Transitions	Apr 1, 2012	ARCConstituency Parsing	—Unverified
Exploiting Lexical Dependencies from Large-Scale Data for Better Shift-Reduce Constituency Parsing	Dec 1, 2012	Constituency Parsing	—Unverified
Constituency Parsing using LLMs	Oct 30, 2023	Constituency Parsing	—Unverified
Entity-Aware Biaffine Attention Model for Improved Constituent Parsing with Reduced Entity Violations	Sep 1, 2024	Constituency ParsingSentence	—Unverified

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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