[PDF] Top 20 Graph based Dependency Parsing with Graph Neural Networks
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Graph based Dependency Parsing with Graph Neural Networks
... powerful tools to collect sentence-level informa- tion, but the representations ignore features related to dependency structures. The biaffine mappings improve vanilla RNNs via a key observation: the ... See full document
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Sentence Level Instance Weighting for Graph Based and Transition Based Dependency Parsing
... Transition-based parsing reduces the problem of finding the most likely dependency tree for a sen- tence to a series of classification problems by see- ing parsing as transitions between ... See full document
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Representing Schema Structure with Graph Neural Networks for Text to SQL Parsing
... a graph, and use graph neural networks (GNNs) to provide a global representation for each node (Li et ...the graph to previ- ously decoded ... See full document
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Effective Greedy Inference for Graph based Non Projective Dependency Parsing
... Ensemble Approaches Finally, several previous works combined dependency parsers. These include Nivre and McDonald (2008) who used the output of one parser to provide features for another, Zhang and Clark (2008) ... See full document
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Session-Based Recommendation with Graph Neural Networks
... Neural-network-based methods, such as NARM and STAMP, outperform the conventional methods, demon- strating the power of adopting deep learning in this do- main. Short/long-term memory models, like GRU4REC ... See full document
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Comparing Non projective Strategies for Labeled Graph Based Dependency Parsing
... non-projective dependency in the gold standard and receive the correct head and label in the parser ...non-projective dependency in the parser output receiving the correct head and ... See full document
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Multi Task Semantic Dependency Parsing with Policy Gradient for Learning Easy First Strategies
... There have been several attempts to train transition-based parsers with reinforcement learn- ing: Zhang and Chan (2009) applied SARSA (Baird III, 1999) to an Arc-Standard model, us- ing SARSA updates to fine-tune ... See full document
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Dependency Parsing with Dilated Iterated Graph CNNs
... Iterated Graph CNNs (DIG-CNNs): a combined convolutional neural network architec- ture and training objective for efficient, end-to-end GPU graph-based dependency ... See full document
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Graph to Sequence Learning using Gated Graph Neural Networks
... name Graph Neural Networks (Gori et ...is based on the architecture proposed by Li et ...input graph it- self such as node classification or path finding while we focus on generating ... See full document
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AntNLP at CoNLL 2018 Shared Task: A Graph Based Parser for Universal Dependency Parsing
... In this paper we describe our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. Our system is based on the deep biaffine neural dependency parser (Dozat and Manning, 2016). The system ... See full document
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The parse is darc and full of errors: Universal dependency parsing with transition based and graph based algorithms
... Universal Dependencies (UD) (Nivre et al., 2016) is a cross-linguistically consistent annota- tion scheme for dependency-based treebanks. UD version 2.0 (UD2) (Nivre et al., 2017b,a) provided the datasets ... See full document
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A Tale of Two Parsers: Investigating and Combining Graph based and Transition based Dependency Parsing
... both graph-based and transition-based ...tiple parsing algorithms is the ensemble approach (Sagae and Lavie, 2006a), which was reported to be useful in improving dependency ... See full document
10
Robust Incremental Neural Semantic Graph Parsing
... statistical parsing has focussed almost exclusively on bilexical depen- dencies or domain-specific logical ...a neural encoder-decoder transition-based parser which is the first full-coverage ... See full document
12
Semantic Dependency Graph Parsing Using Tree Approximations
... Graph parsing by tree approximations and post-processing was most notably performed by the top- performing system of the competition, the one by Du et ...breadth-first graph traversals, possibly ... See full document
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Extracting Narrative Timelines as Temporal Dependency Structures
... two dependency parsing techniques for extracting story timelines and have shown that both outperform a rule- based baseline and a prior-work-inspired pair-wise classification ...dency parsing ... See full document
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A Novel Neural Network Model for Joint POS Tagging and Graph based Dependency Parsing
... Table 2: Official macro-averaged LAS F1 scores of MQuni and baselines from the CoNLL 2017 shared task on UD parsing (Zeman et al., 2017): http://universaldependencies.org/ conll17/results-las.html. “All” refers to ... See full document
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Efficient Inner to outer Greedy Algorithm for Higher order Labeled Dependency Parsing
... predict dependency struc- tures and dependency type labels on each ...most graph-based dependency parsing algorithms only produce unlabeled dependency trees, particularly ... See full document
7
Graph Transformations in Data Driven Dependency Parsing
... improve parsing accuracy has been exploited successfully in statistical parsing systems using constituency-based ...data-driven dependency parsing. Experiments on the Prague ... See full document
8
Improving Graph based Dependency Parsing with Decision History
... The parsing algorithms used in Carreras (2007) independently find the left and right dependents of a word and then combine them later in a bottom- up style based on Eisner ... See full document
9
An Effective Neural Network Model for Graph based Dependency Parsing
... Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment tree- bank. In Proceedings of ... See full document
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