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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 204 papers

TitleStatusHype
Unsupervised Discourse Constituency Parsing Using Viterbi EMCode0
Generalizing Natural Language Analysis through Span-relation RepresentationsCode0
Rethinking Self-Attention: Towards Interpretability in Neural ParsingCode0
A General-Purpose Algorithm for Constrained Sequential Inference—0
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications—0
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders—0
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 Transcripts—0
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic OracleCode0
Cloze-driven Pretraining of Self-attention Networks—0
Unlexicalized Transition-based Discontinuous Constituency ParsingCode0
Investigating NP-Chunking with Universal Dependencies for English—0
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations—0
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing—0
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 Parsing—0
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 Parsing—0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension—0
A Dependency Perspective on RST Discourse Parsing and Evaluation—0
Dialog Generation Using Multi-Turn Reasoning Neural Networks—0
Linear-Time Constituency Parsing with RNNs and Dynamic Programming—0
Gaussian Mixture Latent Vector GrammarsCode0
A New Version of the Sk Treebank of Polish Harmonised with the Walenty Valency Dictionary—0
Coreference Resolution in FreeLing 4.0—0
What's Going On in Neural Constituency Parsers? An AnalysisCode0
Attentive Tensor Product Learning—0
Supervised Attention for Sequence-to-Sequence Constituency Parsing—0
Optimizing for Measure of Performance in Max-Margin Parsing—0
Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages—0
Neural Discontinuous Constituency Parsing—0
A Generative Parser with a Discriminative Recognition Algorithm—0
Effective Inference for Generative Neural Parsing—0
Gradient-based Inference for Networks with Output Constraints—0
Parsing with Traces: An O(n^4) Algorithm and a Structural RepresentationCode0
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects—0
YellowFin and the Art of Momentum TuningCode0
A Minimal Span-Based Neural Constituency Parser—0
Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary TasksCode0
Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing—0
Temporal@ODIL project: Adapting ISO-TimeML to syntactic treebanks for the temporal annotation of spoken speech—0
Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic OraclesCode0
Improving Neural Translation Models with Linguistic Factors—0
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All datasetsPenn TreebankCTB5CTB7ATB

Benchmark Results

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