[PDF] Top 20 Tree Revision Learning for Dependency Parsing
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Tree Revision Learning for Dependency Parsing
... parse tree, either the f irst-best or the one con- structed by a deterministic shift-reduce parser, as in transformation-based ...new learning task whose output space is a set of revision rules and ... See full document
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Incremental Dependency Parsing Using Online Learning
... Algorithm 1 shows the algorithm. It uses an “aggressiveness” parameter C to reduce overfitting, analogous to the C parameter in SVMs. The algo- rithm also needs a cost function ρ, which describes how much a parse ... See full document
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Bayesian Learning for Neural Dependency Parsing
... settings. Dependency parsing may be a natural candidate for Bayesian modeling since a typical sentence can have multiple viable parses cor- responding to different grammatical ambiguities, and considering ... See full document
11
Generalizing Tree Transformations for Inductive Dependency Parsing
... different parsing strategies, which makes them suitable in order to test the parser independence of different ...deterministic parsing strategy, deriving a la- beled dependency structure in a single ... See full document
8
Online Learning for Deterministic Dependency Parsing
... terministic parsing lies in the ability to use the sub- tree information in the features to decide the next ...step. Parsing algorithms which search the entire space (Eisner, 1996; McDonald, 2006) ... See full document
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Sequence Labeling Parsing by Learning across Representations
... into dependency trees using a dependency model, com- pute a probability score for each of them, and fi- nally rerank the most plausible trees based on both ...and dependency parsing (Strzyz et ... See full document
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Learning Reliable Information for Dependency Parsing Adaptation
... Sagae and Tsujii (2007) presented a procedure similar to a single iteration of co-training. Firstly, they trained two parsers on labeled source data. Then the two parsers were used to parse the sen- tences in unlabeled ... See full document
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Deep Multitask Learning for Semantic Dependency Parsing
... Labeled directed graphs are a natural and flexi- ble representation for semantics (Copestake et al., 2005; Baker et al., 2007; Surdeanu et al., 2008; Banarescu et al., 2013, inter alia). Their generality over trees, for ... See full document
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Compositional Semantic Parsing across Graphbanks
... In this paper, we present a single semantic parser that does very well across all of DM, PAS, PSD, EDS and AMR (2015 and 2017). Our system is based on the compositional neural AMR parser of Groschwitz et al. (2018), ... See full document
10
Online Learning of Approximate Dependency Parsing Algorithms
... treating dependency parsing as the search for the highest scoring maximum spanning tree (MST) in a graph yields efficient algorithms for both projective and non-projective ...online learning ... See full document
8
Active Learning for Dependency Parsing with Partial Annotation
... into dependency structures using Penn2Malt with its default head-finding ...many parsing models, we throw out all training sentences longer than 50 to speed up our ... See full document
11
Text level Discourse Dependency Parsing
... blank tree structure, because our parsers and HILDA- manual all perform over 94% of Human and this performance level somewhat reaches a bottle- neck to promote ...perceptron learning algorithm, though ... See full document
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Maximum Spanning Tree Algorithm for Non projective Labeled Dependency Parsing
... a tree results, then this must be the max- imum spanning ...spanning tree on the contracted graph is equivalent to a maximum span- ning tree in the original graph (Leonidas, ... See full document
5
A Pipeline Model for Bottom Up Dependency Parsing
... In this work we used as our learning algorithm a regularized variation of the perceptron update rule as incorporated in SNoW (Roth, 1998; Carlson et al., 1999), a multi-class classifier that is specifically ... See full document
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DTED: Evaluation of Machine Translation Structure Using Dependency Parsing and Tree Edit Distance
... A tree edit distance is a count of the actions re- quired to convert one ordered tree into another. In the manner of Levenshtein distances (Levenshtein, 1965) and Word Error Rate (Nießen et al., 2000), ... See full document
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Tree Based Deterministic Dependency Parsing — An Application to Nivre’s Method —
... We measured the ratio of words assigned correct heads to all words (accuracy), and the ratio of sen- tences with completely correct dependency graphs to all sentences (complete match). In the evalua- tion, we ... See full document
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Active Learning for Dependency Parsing by A Committee of Parsers
... Data-driven dependency parsers need a large an- notated corpus to learn how to generate depen- dency graph of a given ...Active learning is a machine learning approach that al- lows only informative ... See full document
8
Spectral Learning for Non Deterministic Dependency Parsing
... spectral learning methods for non-deterministic split head- automata grammars, a powerful hidden- state formalism for dependency ...a learning algorithm that, like other spectral methods, is ... See full document
11
Cross language Projection of Dependency Trees with Constrained Partial Parsing for Tree to Tree Machine Translation
... For tree-to-string MT, Jiang et ...constituency parsing by guiding the pars- ing procedure of the supervised parser with the projected ...a tree- to-string system (Liu et ...For ... See full document
11
Dependency Parsing by Inference over High recall Dependency Predictions
... two learning-based ap- proaches, we first describe a number of baselines, which provide simple reference scores giving some sense of the difficulty of each ...machine learning systems: 1) an ap- proach that ... See full document
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