[PDF] Top 20 Neural Shift Reduce CCG Semantic Parsing
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Neural Shift Reduce CCG Semantic Parsing
... CCG parsing largely relies on two types of ac- tions: using a lexicon to map words to their cate- gories, and combining categories to acquire the cat- egories of larger ...most semantic pars- ing ... See full document
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Expected F Measure Training for Shift Reduce Parsing with Recurrent Neural Networks
... to parsing with RNN models, including using RNNs (Miikkulainen, 1996; Mayberry and Miikkulainen, 1999; Legrand and Collobert, 2015; Watanabe and Sumita, 2015) and LSTM (Hochreiter and Schmid- huber, 1997) RNNs ... See full document
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Evaluating a Deterministic Shift Reduce Neural Parser for Constituent Parsing
... constituent parsing. This is a more challenging task compared with neural dependency parsing (Table ...the neural network. Second, we find empirically that the neural constituent parser ... See full document
5
SARDSRN: A NEURAL NETWORK SHIFT-REDUCE PARSER
... system [Miikkulainen, 1996] was a first step in this direction. The stack was represented as a compressed distributed rep- resentation, formed by a R AAM (Recursive Auto-Associative Memory) auto-encoding network ... See full document
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Deterministic Shift Reduce Parsing for Unification Based Grammars by Using Default Unification
... grammars, semantic struc- tures, and feature functions becomes ...2005), shift-reduce parsing (Yamada and Matsumoto, 2003), search optimization learning (Daumé and Marcu, 2005) and sampling ... See full document
9
Global Neural CCG Parsing with Optimality Guarantees
... to reduce training ...the neural network is dynamically determined, we do not use ...The neural networks are implemented using the CNN library, 1 and the ... See full document
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Latent Tree Learning with Differentiable Parsers: Shift Reduce Parsing and Chart Parsing
... other neural models have also been proposed which create a tree encoding during pars- ing, but unlike the above architectures rely on tra- ditional parse ... See full document
6
Empirically motivated Generalizations of CCG Semantic Parsing Learning Algorithms
... supervised semantic parser, there has been a recent push towards de- veloping techniques which reduce the annotation cost or the data complexity of the ...will reduce this sample ... See full document
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Encoder Decoder Shift Reduce Syntactic Parsing
... For neural machine translation, such encoder structure has been connected to a corresponding LSTM de- coder, giving the state-of-the-art for sequence- to-sequence ... See full document
10
LSTM CCG Parsing
... reduces parsing times dramatically— outperforming SpaCy, the fastest publicly available parser (Choi et ...of shift-reduce parsers or lexicalized chart parsers, so it is unclear if most other ... See full document
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Exploring Graph-Algebraic CCG Combinators for Syntactic-Semantic AMR Parsing
... the parsing model, which misjudges the probability of the respective scope ...a semantic parser to improve pre- cisely upon these semantically informed syntactic decisions, this behaviour is perhaps to be ... See full document
10
Shift Reduce Constituent Parsing with Neural Lookahead Features
... Our lookahead features are similar in spirit to the pruners of Roark and Hollingshead (2009) and Zhang et al. (2010b), which infer the maximum length of constituents that a particular word can start or end. However, our ... See full document
14
Shift Reduce CCG Parsing with a Dependency Model
... for structures which are evaluated at test time. We develop a novel training tech- nique using a dependency oracle, in which all derivations are hidden. A challenge arises from the fact that the oracle needs to keep ... See full document
10
Shift Reduce CCG Parsing using Neural Network Models
... on neural networks for constituent based parsing (Collobert, 2011; Socher et ...a neural network architecture for dependency ...accurate, parsing around 1000 sentences per second and achieving ... See full document
7
LSTM Shift-Reduce CCG Parsing
... Recently a number of RNN models have been proposed for CCG supertagging (Xu et al., 2015; Lewis et al., 2016; Vaswani et al., 2016; Xu et al., 2016), and such models show dramatic improve- ments over non-recurrent ... See full document
11
Shift Reduce CCG Parsing
... of CCG lexical ...into CCG dependencies or gram- matical relations by a post-processing step, which essentially runs the C&C parser deterministically over the derivation, interpreting the derivation and ... See full document
10
Neural Architectures for Multilingual Semantic Parsing
... In this work, we address multilingual seman- tic parsing – the task of mapping natural lan- guage sentences coming from multiple different languages into their corresponding formal seman- tic representations. We ... See full document
7
Confidence Modeling for Neural Semantic Parsing
... We followed the data preprocessing used in previ- ous work (Dong and Lapata, 2016; Yin and Neu- big, 2017). Input sentences were tokenized us- ing NLTK (Bird et al., 2009) and lowercased. We filtered words that appeared ... See full document
11
Transfer Learning for Neural Semantic Parsing
... Full semantic graphs can be expensive to an- notate, and efforts to date have been fragmented across different formalisms, leading to a limited amount of annotated data in any single ...Using neural ... See full document
9
Deep Neural Models of Semantic Shift
... In this paper, we have built the first diachronic distributional model that represents time as a con- tinuous variable instead of employing data bin- ning. There are several advantages to treating time as continuous. The ... See full document
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