Constituency Parsing
74 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
N-ary Constituent Tree Parsing with Recursive Semi-Markov Model
NP-NET-research/Recursive-Semi-Markov-Model
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ACL 2021
In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children.
Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts
This work explores constituency parsing on automatically recognized transcripts of conversational speech.
Neural Combinatory Constituency Parsing
tmu-nlp/nccp
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Findings (ACL) 2021
We propose two fast neural combinatory models for constituency parsing: binary and multi-branching.
Rule Augmented Unsupervised Constituency Parsing
anshuln/Diora_with_rules
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Findings (ACL) 2021
We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.
Learning Syntax from Naturally-Occurring Bracketings
tzshi/nob-naacl21
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NAACL 2021
Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries.
Nested Named Entity Recognition with Partially-Observed TreeCRFs
FranxYao/Partially-Observed-TreeCRFs
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With the TreeCRF we achieve a uniform way to jointly model the observed and the latent nodes.
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
google-research/google-research
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ACL 2021
There are two major classes of natural language grammar -- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words.
Strongly Incremental Constituency Parsing with Graph Neural Networks
yzhangcs/parser
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NeurIPS 2020
Based on our transition system, we develop a strongly incremental parser.
Improving Constituency Parsing with Span Attention
cuhksz-nlp/SAPar
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Findings of the Association for Computational Linguistics 2020
Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task.
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?
YianZhang/ONLSTM-analysis
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EMNLP (BlackboxNLP) 2020
Recent latent tree learning models can learn constituency parsing without any exposure to human-annotated tree structures.
