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

Showing 101150 of 204 papers

TitleStatusHype
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders0
A General-Purpose Algorithm for Constrained Sequential Inference0
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications0
Cross-Domain Generalization of Neural Constituency ParsersCode0
Head-Driven Phrase Structure Grammar Parsing on Penn TreebankCode0
Sequence Labeling Parsing by Learning Across RepresentationsCode0
PTB Graph Parsing with Tree ApproximationCode0
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-EncodersCode0
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time InferenceCode0
Neural Constituency Parsing of Speech Transcripts0
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive AutoencodersCode1
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic OracleCode0
Cloze-driven Pretraining of Self-attention Networks0
Unlexicalized Transition-based Discontinuous Constituency ParsingCode0
Multilingual Constituency Parsing with Self-Attention and Pre-TrainingCode1
Investigating NP-Chunking with Universal Dependencies for English0
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations0
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing0
Direct Output Connection for a High-Rank Language ModelCode0
Grammar Induction with Neural Language Models: An Unusual ReplicationCode0
An Empirical Investigation of Error Types in Vietnamese Parsing0
An Empirical Study of Building a Strong Baseline for Constituency ParsingCode0
Straight to the Tree: Constituency Parsing with Neural Syntactic DistanceCode0
Policy Gradient as a Proxy for Dynamic Oracles in Constituency Parsing0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension0
Dialog Generation Using Multi-Turn Reasoning Neural Networks0
A Dependency Perspective on RST Discourse Parsing and Evaluation0
Linear-Time Constituency Parsing with RNNs and Dynamic Programming0
Gaussian Mixture Latent Vector GrammarsCode0
Constituency Parsing with a Self-Attentive EncoderCode1
A New Version of the Sk Treebank of Polish Harmonised with the Walenty Valency Dictionary0
Coreference Resolution in FreeLing 4.00
What's Going On in Neural Constituency Parsers? An AnalysisCode0
Attentive Tensor Product Learning0
Supervised Attention for Sequence-to-Sequence Constituency Parsing0
Optimizing for Measure of Performance in Max-Margin Parsing0
Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages0
Neural Discontinuous Constituency Parsing0
A Generative Parser with a Discriminative Recognition Algorithm0
Effective Inference for Generative Neural Parsing0
Gradient-based Inference for Networks with Output Constraints0
Parsing with Traces: An O(n^4) Algorithm and a Structural RepresentationCode0
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects0
YellowFin and the Art of Momentum TuningCode0
A Minimal Span-Based Neural Constituency Parser0
Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary TasksCode0
Temporal@ODIL project: Adapting ISO-TimeML to syntactic treebanks for the temporal annotation of spoken speech0
Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing0
Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic OraclesCode0
Improving Neural Translation Models with Linguistic Factors0
Show:102550
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Benchmark Results

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