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 151–200 of 204 papers

TitleStatusHype
Boosting for Efficient Model Selection for Syntactic Parsing—0
What Do Recurrent Neural Network Grammars Learn About Syntax?Code0
Parsing as Language ModelingCode0
Probabilistic Graph-based Dependency Parsing with Convolutional Neural NetworkCode0
Incremental Parsing with Minimal Features Using Bi-Directional LSTM—0
Should Have, Would Have, Could Have. Investigating Verb Group Representations for Parsing with Universal Dependencies.Code0
Discontinuity (Re) ^2-visited: A Minimalist Approach to Pseudoprojective Constituent Parsing—0
Construction of an English Dependency Corpus incorporating Compound Function Words—0
Recurrent Neural Network GrammarsCode0
Statistical Parsing by Machine Learning from a Classical Arabic Treebank—0
Syntactic Parse FusionCode0
Exploring Confidence-based Self-training for Multilingual Dependency Parsing in an Under-Resourced Language Scenario—0
Domain Adaptation for Dependency Parsing via Self-Training—0
Identifying Cascading Errors using Constraints in Dependency Parsing—0
Scheduled Sampling for Sequence Prediction with Recurrent Neural NetworksCode0
DeepNL: a Deep Learning NLP pipeline—0
Shift-Reduce Constituency Parsing with Dynamic Programming and POS Tag Lattice—0
Grammar as a Foreign LanguageCode0
The Effect of Dependency Representation Scheme on Syntactic Language Modelling—0
A Fast and Accurate Dependency Parser using Neural Networks—0
Left-corner Transitions on Dependency Parsing—0
How much do word embeddings encode about syntax?—0
Less Grammar, More FeaturesCode0
Chunking Clinical Text Containing Non-Canonical Language—0
Strategies for Contiguous Multiword Expression Analysis and Dependency Parsing—0
Self-training a Constituency Parser using n-gram Trees—0
Discosuite - A parser test suite for German discontinuous structures—0
Constituency Parsing of Bulgarian: Word- vs Class-based Parsing—0
A Multi-Teraflop Constituency Parser using GPUs—0
Online Learning for Inexact Hypergraph Search—0
Representation of Morphosyntactic Units and Coordination Structures in the Turkish Dependency Treebank—0
The LIGM-Alpage architecture for the SPMRL 2013 Shared Task: Multiword Expression Analysis and Dependency Parsing—0
(Re)ranking Meets Morphosyntax: State-of-the-art Results from the SPMRL 2013 Shared Task—0
Part-of-Speech Induction in Dependency Trees for Statistical Machine Translation—0
Bilingually-Guided Monolingual Dependency Grammar Induction—0
Dependency Parser Adaptation with Subtrees from Auto-Parsed Target Domain Data—0
Iterative Transformation of Annotation Guidelines for Constituency Parsing—0
Parsing Russian: a hybrid approach—0
Syntactic Identification of Occurrences of Multiword Expressions in Text using a Lexicon with Dependency Structures—0
Knowledge Sources for Constituent Parsing of German, a Morphologically Rich and Less-Configurational Language—0
Word Segmentation, Unknown-word Resolution, and Morphological Agreement in a Hebrew Parsing System—0
The French Social Media Bank: a Treebank of Noisy User Generated Content—0
Exploiting Lexical Dependencies from Large-Scale Data for Better Shift-Reduce Constituency Parsing—0
Discriminative Strategies to Integrate Multiword Expression Recognition and Parsing—0
Assigning Deep Lexical Types Using Structured Classifier Features for Grammatical Dependencies—0
FBK: Machine Translation Evaluation and Word Similarity metrics for Semantic Textual Similarity—0
An Empirical Investigation of Statistical Significance in NLP—0
A Feature-Rich Constituent Context Model for Grammar Induction—0
Vine Pruning for Efficient Multi-Pass Dependency Parsing—0
MSR SPLAT, a language analysis toolkit—0
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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