[PDF] Top 20 Experiments with Generative Models for Dependency Tree Linearization
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Experiments with Generative Models for Dependency Tree Linearization
... consider generative de- pendency models from the perspective of word ...such models. In addition, the use of proba- bilistic models means that we can easily define well-motivated normalized ... See full document
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Synthetic Text Generation for Sentiment Analysis
... different generative models for text generation, which maintain spe- cific properties of natural language text, ...of experiments using different data sets and sentiment analysis methods, we show ... See full document
6
BinLin: A Simple Method of Dependency Tree Linearization
... sitive to the different values of hyperparameters; its performance has the highest standard devia- tion among all models, which is most likely due to the same sample complexity issue. Interest- ingly enough, on ... See full document
16
Enhancing Unsupervised Generative Dependency Parser with Contextual Information
... unsupervised dependency parsers are based on probabilistic generative models that learn the joint distribution of the given sentence and its ...ative models usually explicit decompose the ... See full document
11
Experiments for Dependency Parsing of Greek
... Following recent efforts in exploiting automatically processed data in training (Chen et al., 2012) and in accelerating treebank creation (Lynn et al., 2012), we conducted an experiment in extending the training set with ... See full document
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A System for Experiments with Dependency Parsers
... MSTParser models we selected the two best per- forming models on average with major difference in the scope of ...a tree without post-processing by adding arcs between the two top nodes on the ... See full document
5
Large scale Word Alignment Using Soft Dependency Cohesion Constraints
... disjoint dependency subtrees in the source language generally do not overlap in the target ...discriminative models, which is ineffective for large-scale ...take dependency cohesion as a soft ... See full document
10
A Bayesian Model for Generative Transition based Dependency Parsing
... We propose a simple, scalable, fully generative model for transition-based de- pendency parsing with high accuracy. The model, parameterized by Hierarchical Pitman-Yor Processes, overcomes the lim- itations of ... See full document
10
Comparing Top Down and Bottom Up Neural Generative Dependency Models
... proposed models predict structure and words jointly, and the predicted syn- tactic structure is used to determine the structure of the neural network that is used to represent the history of actions taken by the ... See full document
11
Hidden Markov Tree Model for Word Alignment
... Markov Tree (HMT) ...a tree structure which is isomorphic to the target dependency tree and models the dis- tortion probability based on the source de- pendency tree, thereby ... See full document
9
Sentence Realization with Unlexicalized Tree Linearization Grammars
... generation models. In particular, the now ever-so-popular dependency representation for syntacto-semantic structures has made its way into the sentence realization task, as evident by the recent Generation ... See full document
10
Text level Discourse Dependency Parsing
... Since dependency trees contain much fewer nodes and on average they are simpler than constituency based trees, the current dependency parsers can have a relatively low computational ...cerning ... See full document
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A Probabilistic Generative Model for an Intermediate Constituency Dependency Representation
... This section describes the probabilistic generative model which was implemented in order to dis- ambiguate TDS structures. We have chosen the same strategy we have described in (Sangati et al., 2009). The idea ... See full document
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Generative Constituent Parsing and Discriminative Dependency Reranking: Experiments on English and French
... this approach lies in its complexity. The constraints can, theoretically, range over any aspect of the final structures, which prevents from using efficient dy- namic programming techniques when searching for a global ... See full document
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Generative Incremental Dependency Parsing with Neural Networks
... Generative models for graph-based dependency parsing (Eisner, 1996; Wallach et ...language models based on PCFGs (Roark, 2001; Charniak, 2001) and incre- mental parsing (Chelba and Jelinek, ... See full document
7
Sentence Realisation from Bag of Words with Dependency Constraints
... MT 1 (Lavie et al., 2003), the source sentence is first analyzed by a parser (a phrase-structure or a dependency-based parser). Then the source lexical items are transferred to the target language using a ... See full document
6
Unsupervised Recurrent Neural Network Grammars
... different models on ...the tree LSTM composition function (a left branching tree, which always performs REDUCE when possible, is the “deepest” ... See full document
13
Dependency Trees and the Strong Generative Capacity of CCG
... strong generative capacity of PF-CCG will be useful to transfer algorithms and linguistic insights between ...regular tree languages— given our results, to CCG in ...same tree languages by ... See full document
9
Phylogenic Multi Lingual Dependency Parsing
... We have applied this framework to dependency parsing using a graph-based neural parser and the phylogenetic tree of the languages from UD 2.2 to guide the training process. Our results show that ... See full document
12
Online Learning of Approximate Dependency Parsing Algorithms
... (or tree) constraint we have previously required on dependency ...acyclic dependency graphs here. Though less common than trees, dependency graphs involving multiple parents are well ... See full document
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