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
A Conditional Splitting Framework for Efficient Constituency Parsing—0
Self-training a Constituency Parser using n-gram Trees—0
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations—0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management—0
Shift-Reduce Constituency Parsing with Dynamic Programming and POS Tag Lattice—0
Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(n\^6) down to O(n\^3)—0
Statistical Parsing by Machine Learning from a Classical Arabic Treebank—0
Strategies for Contiguous Multiword Expression Analysis and Dependency Parsing—0
Supervised Attention for Sequence-to-Sequence Constituency Parsing—0
Syntactic Identification of Occurrences of Multiword Expressions in Text using a Lexicon with Dependency Structures—0
Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages—0
Temporal@ODIL project: Adapting ISO-TimeML to syntactic treebanks for the temporal annotation of spoken speech—0
The Effect of Dependency Representation Scheme on Syntactic Language Modelling—0
The French Social Media Bank: a Treebank of Noisy User Generated Content—0
The LIGM-Alpage architecture for the SPMRL 2013 Shared Task: Multiword Expression Analysis and Dependency Parsing—0
The Limitations of Limited Context for Constituency Parsing—0
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing—0
Unity in Diversity: A Unified Parsing Strategy for Major Indian Languages—0
Universal Recurrent Neural Network Grammar—0
Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence—0
Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence—0
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders—0
Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders—0
Vine Pruning for Efficient Multi-Pass Dependency Parsing—0
Word Segmentation as Unsupervised Constituency Parsing—0
Word Segmentation, Unknown-word Resolution, and Morphological Agreement in a Hebrew Parsing System—0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension—0
Entity-Aware Biaffine Attention Model for Improved Constituent Parsing with Reduced Entity Violations—0
Exploiting Lexical Dependencies from Large-Scale Data for Better Shift-Reduce Constituency Parsing—0
Exploring Confidence-based Self-training for Multilingual Dependency Parsing in an Under-Resourced Language Scenario—0
Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing—0
Fast Unsupervised Dependency Parsing with Arc-Standard Transitions—0
FBK: Machine Translation Evaluation and Word Similarity metrics for Semantic Textual Similarity—0
Genres, Parsers, and BERT: The Interaction Between Parsers and BERT Models in Cross-Genre Constituency Parsing in English and Swedish—0
Gradient-based Inference for Networks with Output Constraints—0
GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer—0
Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads—0
How much do word embeddings encode about syntax?—0
Identifying Cascading Errors using Constraints in Dependency Parsing—0
Improving Low-resource RRG Parsing with Cross-lingual Self-training—0
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects—0
Improving Neural Translation Models with Linguistic Factors—0
Incremental Parsing with Minimal Features Using Bi-Directional LSTM—0
Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency ParsingCode0
Ensemble Distillation for Unsupervised Constituency ParsingCode0
Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken SentencesCode0
Should Have, Would Have, Could Have. Investigating Verb Group Representations for Parsing with Universal Dependencies.Code0
Simple Hardware-Efficient PCFGs with Independent Left and Right ProductionsCode0
Effective Self-Training for ParsingCode0
Sketch-Guided Constrained Decoding for Boosting Blackbox Large Language Models without Logit AccessCode0
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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