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Constituency Parsing

18 papers with code · Natural Language Processing

Consituency 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)).

State-of-the-art leaderboards

Greatest papers with code

Attention Is All You Need

NeurIPS 2017 tensorflow/tensor2tensor

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.

CONSTITUENCY PARSING MACHINE TRANSLATION

Grammar as a Foreign Language

NeurIPS 2015 atpaino/deep-text-corrector

Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades.

CONSTITUENCY PARSING

YellowFin and the Art of Momentum Tuning

ICLR 2018 JianGoForIt/YellowFin

We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam.

CONSTITUENCY PARSING LANGUAGE MODELLING

Multilingual Constituency Parsing with Self-Attention and Pre-Training

31 Dec 2018nikitakit/self-attentive-parser

We extend our previous work on constituency parsing (Kitaev and Klein, 2018) by incorporating pre-training for ten additional languages, and compare the benefits of no pre-training, ELMo (Peters et al., 2018), and BERT (Devlin et al., 2018).

CONSTITUENCY PARSING

Constituency Parsing with a Self-Attentive Encoder

ACL 2018 nikitakit/self-attentive-parser

We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-of-the-art discriminative constituency parser.

CONSTITUENCY PARSING

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

NeurIPS 2015 Chung-I/Variational-Recurrent-Autoencoder-Tensorflow

Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning.

CONSTITUENCY PARSING IMAGE CAPTIONING SPEECH RECOGNITION

Recurrent Neural Network Grammars

HLT 2016 clab/rnng

We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure.

CONSTITUENCY PARSING LANGUAGE MODELLING

Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles

EMNLP 2016 jhcross/span-parser

Parsing accuracy using efficient greedy transition systems has improved dramatically in recent years thanks to neural networks.

CONSTITUENCY PARSING DEPENDENCY PARSING