[PDF] Top 20 Improving Graph based Dependency Parsing with Decision History
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Improving Graph based Dependency Parsing with Decision History
... prove graph-based dependency parsing by using decision ...the dependency relations between words with a short distance are more reliable than ones between words with a long ... See full document
9
Improving Dependency Parsing with Interlinear Glossed Text and Syntactic Projection
... Due to linguistic divergence, projected trees are error-prone. Instead of making hard decisions based on projection, we use information from projected trees as a feature in a discriminative parser. This feature ... See full document
10
Improving Semantic Dependency Parsing with Syntactic Features
... Semantic dependency parsers are typically con- ceptualized as systems for structured prediction, combining a data-driven component that learns how to score dependency graphs with a decoder that retrieves ... See full document
10
Improving Dependency Parsing with Fined Grained Features
... Some researchers conducted experiments on CTB with a different data split: files 1-815 and files 1001-1136 for training, files 816-885 and files 1137-1147 for test, files 886-931 and 1148-1151 for development. The ... 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
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Improving Dependency Parsing with Semantic Classes
... MaltParser (Nivre et al. 2006) is a trainable de- pendency parser that has been successfully applied to typologically different languages and treebanks. We will use one of its standard versions (version 1.4). The parser ... See full document
5
Effective Greedy Inference for Graph based Non Projective Dependency Parsing
... the form of advanced optimization techniques such as dual decomposition (Martins et al., 2013), LP re- laxations (Riedel et al., 2012), belief propagation (Smith and Eisner, 2008; Gormley et al., 2015) and sampling ... See full document
10
An Effective Neural Network Model for Graph based Dependency Parsing
... Dependency parsing is essential for computers to understand natural languages, whose performance may have a direct effect on many NLP applica- ...importance, dependency parsing, has been ... See full document
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Probabilistic Graph based Dependency Parsing with Convolutional Neural Network
... the representation from basic models), add or mul means to use which way for attaching distance in- formation. Surprisingly, simple adding method surpasses the more complex multiplication-with- activation method, which ... See full document
11
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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Incremental Graph based Neural Dependency Parsing
... for graph- based neural dependency parsing systems, they either count on the long-term mem- ory and attention mechanism to implicitly capture the high-order features or give up the global ... See full document
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Graph based Dependency Parsing with Bidirectional LSTM
... Dependency parsing is a fundamental task for lan- guage processing which has been investigated for ...of dependency parsing models, graph-based models are attractive for their ... See full document
10
Dependency Parsing with Dilated Iterated Graph CNNs
... Stacking many dilated CNN layers can easily in- corporate information from a whole sentence. For example, with a radius of 1 and 4 layers of dilated convolutions, the effective input window size for each token is width ... See full document
6
Graph based Dependency Parsing with Graph Neural Networks
... Here we focus on graph-based dependency parsers. Given a sentence, a parser first scores all word pairs about how possible they hold valid de- pendency relations, and then use decoders (e.g., greedy, ... See full document
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An Empirical Investigation of Structured Output Modeling for Graph based Neural Dependency Parsing
... neural graph parsers uti- lized structured techniques (Pei et ...BiAF parsing model. In this paper, building upon the BiAF based neural architecture, we empirically investigate the effec- tiveness of ... 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
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
10
Improving Arabic Dependency Parsing with Form based and Functional Morphological Features
... eight dependency re- lations: SBJ and OBJ for subject and (direct or indi- rect) object, respectively, (whether they appear pre- or post-verbally); IDF for the idafa (possessive) re- lation; MOD for most other ... See full document
11
Improving Chinese POS Tagging with Dependency Parsing
... Table 3: The number of error patterns made by dif- ferent models. An error pattern “X → Y” means that the focus word, whose true tag is ‘X’, is as- signed a tag ‘Y’. “CRF” refers to the baseline CRF-based model. ... See full document
5
Semantic Dependency Graph Parsing Using Tree Approximations
... Deletion and trimming Converting reentrant graphs to trees requires edge removal. The basic idea of removing edges in pre-processing and trying to reconstruct them in post-processing is at the core of tree ... See full document
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