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

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Showing 101–150 of 204 papers

TitleStatusHype
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
A Conditional Splitting Framework for Efficient Constituency Parsing—0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension—0
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications—0
A Dependency Perspective on RST Discourse Parsing and Evaluation—0
A Fast and Accurate Dependency Parser using Neural Networks—0
A Feature-Rich Constituent Context Model for Grammar Induction—0
A General-Purpose Algorithm for Constrained Sequential Inference—0
A Generative Parser with a Discriminative Recognition Algorithm—0
A Minimal Span-Based Neural Constituency Parser—0
A Multi-Teraflop Constituency Parser using GPUs—0
An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese—0
An Empirical Comparison of Unsupervised Constituency Parsing Methods—0
An Empirical Investigation of Error Types in Vietnamese Parsing—0
An Empirical Investigation of Statistical Significance in NLP—0
An Empirical Study for Vietnamese Constituency Parsing with Pre-training—0
A New Version of the Sk Treebank of Polish Harmonised with the Walenty Valency Dictionary—0
Assigning Deep Lexical Types Using Structured Classifier Features for Grammatical Dependencies—0
A treebank-based study on the influence of Italian word order on parsing performance—0
Attentive Tensor Product Learning—0
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization—0
A Warm Start and a Clean Crawled Corpus -- A Recipe for Good Language Models—0
A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models—0
Bilingually-Guided Monolingual Dependency Grammar Induction—0
Boosting for Efficient Model Selection for Syntactic Parsing—0
Chunking Clinical Text Containing Non-Canonical Language—0
Cloze-driven Pretraining of Self-attention Networks—0
Constituency Parsing of Bulgarian: Word- vs Class-based Parsing—0
Constituency Parsing using LLMs—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
8NFC + BERT-largeF1 score95.92—Unverified
9N-ary semi-markov + 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