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 51–100 of 204 papers

TitleStatusHype
An Empirical Investigation of Statistical Significance in NLP—0
Dialog Generation Using Multi-Turn Reasoning Neural Networks—0
Gradient-based Inference for Networks with Output Constraints—0
Bilingually-Guided Monolingual Dependency Grammar Induction—0
Discontinuity (Re) ^2-visited: A Minimalist Approach to Pseudoprojective Constituent Parsing—0
Boosting for Efficient Model Selection for Syntactic Parsing—0
A treebank-based study on the influence of Italian word order on parsing performance—0
Discriminative Strategies to Integrate Multiword Expression Recognition and Parsing—0
Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction—0
Domain Adaptation for Dependency Parsing via Self-Training—0
MSR SPLAT, a language analysis toolkit—0
Effective Inference for Generative Neural Parsing—0
Neural Discontinuous Constituency Parsing—0
Assigning Deep Lexical Types Using Structured Classifier Features for Grammatical Dependencies—0
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications—0
Learning Syntactic and Dynamic Selective Encoding for Document Summarization—0
Unsupervised Parsing by Searching for Frequent Word Sequences among Sentences with Equivalent Predicate-Argument Structures—0
Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing—0
A Multi-Teraflop Constituency Parser using GPUs—0
Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing—0
Exploring Confidence-based Self-training for Multilingual Dependency Parsing in an Under-Resourced Language Scenario—0
Exploiting Lexical Dependencies from Large-Scale Data for Better Shift-Reduce 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
Constituency Parsing using LLMs—0
Construction of an English Dependency Corpus incorporating Compound Function Words—0
Entity-Aware Biaffine Attention Model for Improved Constituent Parsing with Reduced Entity Violations—0
Genres, Parsers, and BERT: The Interaction Between Parsers and BERT Models in Cross-Genre Constituency Parsing in English and Swedish—0
Constituency Parsing of Bulgarian: Word- vs Class-based Parsing—0
Coreference Resolution in FreeLing 4.0—0
Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency—0
GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer—0
A Minimal Span-Based Neural Constituency Parser—0
jp-evalb: Robust Alignment-based PARSEVAL Measures—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
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization—0
Cloze-driven Pretraining of Self-attention Networks—0
Improving Low-resource RRG Parsing with Cross-lingual Self-training—0
A Fast and Accurate Dependency Parser using Neural Networks—0
Improving Neural Translation Models with Linguistic Factors—0
Efficient Constituency Parsing by Pointing—0
Chunking Clinical Text Containing Non-Canonical Language—0
Incremental Parsing with Minimal Features Using Bi-Directional LSTM—0
Deep Inside-outside Recursive Autoencoder with All-span Objective—0
Investigating Non-local Features for Neural Constituency Parsing—0
Investigating NP-Chunking with Universal Dependencies for English—0
Iterative Transformation of Annotation Guidelines for Constituency Parsing—0
A New Version of the Sk Treebank of Polish Harmonised with the Walenty Valency Dictionary—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