Constituency Parsing
73 papers with code • 4 benchmarks • 6 datasets
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)
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+-------------+------------+
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Noun (N) Verb Phrase (VP)
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John +-------+--------+
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Verb (V) Noun (N)
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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)).
Benchmarks
These leaderboards are used to track progress in Constituency Parsing
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Penn Treebank
FLUE
Taiga Corpus
Latest papers
Court Judgement Labeling Using Topic Modeling and Syntactic Parsing
In regions that practice common law, relevant historical cases are essential references for sentencing.
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
magiccircuit/treemix
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NAACL 2022
Data augmentation is an effective approach to tackle over-fitting.
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing
louchao98/nner_as_parsing
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ACL 2022
They treat nested entities as partially-observed constituency trees and propose the masked inside algorithm for partial marginalization.
CPTAM: Constituency Parse Tree Aggregation Method
This paper adopts the truth discovery idea to aggregate constituency parse trees from different parsers by estimating their reliability in the absence of ground truth.
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks
sustcsonglin/pointer-net-for-nested
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ACL 2022
Constituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans.
Investigating Non-local Features for Neural Constituency Parsing
ringos/nfc-parser
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ACL 2022
Besides, our method achieves state-of-the-art BERT-based performance on PTB (95. 92 F1) and strong performance on CTB (92. 31 F1).
Dependency Induction Through the Lens of Visual Perception
ruisi-su/concrete_dep
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CoNLL (EMNLP) 2021
Our experiments find that concreteness is a strong indicator for learning dependency grammars, improving the direct attachment score (DAS) by over 50\% as compared to state-of-the-art models trained on pure text.
Improved Latent Tree Induction with Distant Supervision via Span Constraints
iesl/distantly-supervised-diora
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EMNLP 2021
For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing.
ELIT: Emory Language and Information Toolkit
emorynlp/elit
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We introduce ELIT, the Emory Language and Information Toolkit, which is a comprehensive NLP framework providing transformer-based end-to-end models for core tasks with a special focus on memory efficiency while maintaining state-of-the-art accuracy and speed.
Headed-Span-Based Projective Dependency Parsing
sustcsonglin/span-based-dependency-parsing
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ACL 2022
In a projective dependency tree, the largest subtree rooted at each word covers a contiguous sequence (i. e., a span) in the surface order.
