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
Structured Tree Alignment for Evaluation of (Speech) Constituency Parsing
We present the structured average intersection-over-union ratio (STRUCT-IOU), a similarity metric between constituency parse trees motivated by the problem of evaluating speech parsers.
LLM-enhanced Self-training for Cross-domain Constituency Parsing
jianlingl/llm_st_constparsing
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Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM's performance.
Simple Hardware-Efficient PCFGs with Independent Left and Right Productions
Scaling dense PCFGs to thousands of nonterminals via a low-rank parameterization of the rule probability tensor has been shown to be beneficial for unsupervised parsing.
DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit
This work introduces the novel task of nested compound type identification (NeCTI), which aims to identify nested spans of a multi-component compound and decode the implicit semantic relations between them.
Ensemble Distillation for Unsupervised Constituency Parsing
We investigate the unsupervised constituency parsing task, which organizes words and phrases of a sentence into a hierarchical structure without using linguistically annotated data.
Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning
In this work, we demonstrate that formal grammars can describe the output space for a much wider range of tasks and argue that GCD can serve as a unified framework for structured NLP tasks in general.
Approximating CKY with Transformers
ghazalkhalighinejad/approximating-cky
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We investigate the ability of transformer models to approximate the CKY algorithm, using them to directly predict a sentence's parse and thus avoid the CKY algorithm's cubic dependence on sentence length.
Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences
roger-tseng/speech-parsing
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The goal is to determine the spoken sentences' hierarchical syntactic structure in the form of constituency parse trees, such that each node is a span of audio that corresponds to a constituent.
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars
We study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing.
On Parsing as Tagging
rycolab/parsing-as-tagging
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There have been many proposals to reduce constituency parsing to tagging in the literature.
