[PDF] Top 20 SARDSRN: A NEURAL NETWORK SHIFT-REDUCE PARSER
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SARDSRN: A NEURAL NETWORK SHIFT-REDUCE PARSER
... a shift- reduce parser in the following way: the network is trained to step through the parse (such as that in figure 1), generat- ing a compressed distributed representation of the top ... See full document
6
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later
... To overcome this problem, DINN estimates a correctness probability after every Shift action. This output is trained to discriminate correct from incorrect parse prefixes, using the same hidden representation as ... See full document
9
Neural Shift Reduce CCG Semantic Parsing
... deep neural architectures for decision making in linear- time dependency parsing (Chen and Manning, 2014; Dyer et ...apply shift-reduce parsing to semantic ... See full document
12
Discriminative Training of a Neural Network Statistical Parser
... the network being trained to choose these top parses, training times were very long and the resulting networks did not outperform their generative ...a network of the same type which had been trained with ... See full document
8
Shift Reduce Constituent Parsing with Neural Lookahead Features
... recurrent neural networks (RNNs) are remarkably effective models to encode the full input ...a neural language model de- coder generates output sentences for machine trans- lation (Bahdanau et ... See full document
14
Incremental Recurrent Neural Network Dependency Parser with Search based Discriminative Training
... a shift-reduce parsing architecture so that they can use search based decoding algorithms with effective pruning ...discriminative neural net- work shift-reduce parser, which is ... See full document
11
A Dependency Perspective on RST Discourse Parsing and Evaluation
... the shift-reduce RST parser of Sagae ...a shift-reduce parser that jointly learns to predict transitions and to project the bag-of-word representation of each EDU into a latent ... See full document
39
TAG Parsing with Neural Networks and Vector Representations of Supertags
... a shift-reduce parsing model based on a feed- forward neural network that makes use of dense supertag ...to shift- reduce CCG parsing taken by Zhang and Clark (2011), it differs ... See full document
11
Expected F Measure Training for Shift Reduce Parsing with Recurrent Neural Networks
... and neural network models (Chen and Manning, 2014), accurate and efficient shift-reduce parsing models can be obtained with little feature engineering, largely alleviating the feature sparsity ... See full document
11
Low Resource Dependency Parsing: Cross lingual Parameter Sharing in a Neural Network Parser
... supervised neural network parser as men- tioned in Section 2 on the Universal Dependency English treebank using UPOS ...English parser is ...English parser as the prior for our ... See full document
6
Shift Reduce CCG Parsing using Neural Network Models
... Shift-reduce parsing is interesting for practical real- world applications like parsing the web, since pars- ing can be achieved in linear time. Although greedy parsers are fast, accuracies of these parsers ... See full document
7
Evaluating a Deterministic Shift Reduce Neural Parser for Constituent Parsing
... the neural network into a two-layer hierarchical softmax, the action layer and the label ...optimal shift-reduce action type, which resolves structural ambiguities, and then find the optimal ... See full document
5
A Shift Reduce Dependency Parser Based on Reinforcement Learning
... This paper uses English and Chinese corpus provided by Universal Dependency as the data set. Besides, this paper uses the external large corpus to train the word2vec vectors and the part of the speech vectors. The whole ... See full document
8
Representing Schema Structure with Graph Neural Networks for Text to SQL Parsing
... At a high-level our model has the following parts (Figure 3). (a) The schema is converted to a graph. (b) The graph is softly pruned conditioned on the input question. (c) A Graph neural network generates a ... See full document
6
Translation Rules and ANN based model for English to Urdu Machine Translation
... The neural networks model for grammar structure rules gives the Urdu equivalent grammatical structure to English sen- tence being translated and the neural model for bilin- gual knowledgeable dictionary ... See full document
12
Transition Based Parsing for Deep Dependency Structures
... For syntactic parsing, ensembled methods have been shown to be very helpful in boosting accuracy (Sagae and Lavie 2006; Zhang et al. 2009; McDonald and Nivre 2011). In particular, Surdeanu and Manning (2010) presented a ... See full document
37
Deterministic Shift Reduce Parsing for Unification Based Grammars by Using Default Unification
... deterministic shift-reduce parsing proposed in this paper. “back𝑛” means shift-reduce pars- ing with backtracking at most 𝑛 times for each ...best-first shift-reduce parsing with ... See full document
9
Hybrid Parsing: Using Probabilistic Models as Predictors for a Symbolic Parser
... Another reason for considering hybrid ap- proaches is the influence that contextual factors might exert on the process of determining the most plausible sentence interpretation. Since this influ- ence is dynamically ... See full document
8
Shift Reduce CCG Parsing
... more shift actions – a shift action for each word-lexical category ...C&C parser solves this problem by building the complete packed chart consistent with the lexical categories supplied by the ... See full document
10
DSRNet: A Novel Feature Extraction Network Achieving Trade off between Accuracy and Speed
... to reduce the computation complexity while maintaining the accuracy of convolution neural ...further reduce the network complexity while ensuring the ...extraction network called DSRNet ... See full document
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