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persian-sentiment-analysis

Transition-Based Dependency Parsing

3260 papers • 126 benchmarks • 313 datasets

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Transition-Based Dependency Parsing with Stack Long Short-Term Memory

Chris Dyer, Noah A. Smith, Wang Ling, Miguel Ballesteros, Austin Matthews•Thu May 28 2015

This work was sponsored in part by the U. S. Army Research Laboratory and the NSF CAREER grant IIS-1054319 and the European Commission.

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Template Kernels for Dependency Parsing

Yoav Goldberg, A. Globerson, Hillel Taub-Tabib•Wed Dec 31 2014

This work presents a novel kernel which facilitates efficient parsing with feature representations corresponding to a much larger set of combinations, and integrates into a parse reranking system and demonstrates its effectiveness on four languages from the CoNLL-X shared task.

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A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing

Mark Johnson, Dat Quoc Nguyen, M. Dras•Mon May 15 2017

A novel neural network model is presented that outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art.

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Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States

Ashish Vaswani, Kenji Sagae•Wed Jun 01 2016

This paper proposes a new approach for approximate structured inference for transition-based parsing that produces scores suitable for global scoring using local models with the introduction of error states in local training.

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Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing

Zhuosheng Zhang, Zhao Hai, Z. Li, Shexia He•Sun Dec 31 2017

This system predicts the part-of-speech tag and dependency tree jointly and achieves a macro-average of 68.31% LAS F1 score, with an improvement of 2.51% compared with the UDPipe.

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Tree-Stack LSTM in Transition Based Dependency Parsing

Deniz Yuret, Erenay Dayanik, Ömer Kirnap•Sun Dec 31 2017

We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks. Tree-stack LSTM does not use any parse tree based or hand-crafted features, yet performs better than models with these features. We also develop new set of embeddings from raw features to enhance the performance. There are 4 main components of this model: stack’s σ-LSTM, buffer’s β-LSTM, actions’ LSTM and tree-RNN. All LSTMs use continuous dense feature vectors (embeddings) as an input. Tree-RNN updates these embeddings based on transitions. We show that our model improves performance with low resource languages compared with its predecessors. We participate in CoNLL 2018 UD Shared Task as the “KParse” team and ranked 16th in LAS, 15th in BLAS and BLEX metrics, of 27 participants parsing 82 test sets from 57 languages.

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Bidirectional Transition-Based Dependency Parsing

Kewei Tu, Y. Yuan, Yong Jiang•Tue Jul 16 2019

Empirical results show that the proposed simple framework for bidirectional transitionbased parsing methods lead to competitive parsing accuracy and the method based on dynamic oracle consistently achieves the best performance.

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Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set

Tianze Shi, Liang Huang, Lillian Lee•Mon Jul 31 2017

A minimal feature set for transition-based dependency parsing is presented and the best unlabeled attachment score reported on the Chinese Treebank and the “second-best-in-class” result on the English Penn Treebank are achieved.

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Distilling Knowledge for Search-based Structured Prediction

Ting Liu, Bing Qin, Yijia Liu, Wanxiang Che, Huaipeng Zhao•Mon May 28 2018

Experimental results show that distillation can effectively improve the single model’s performance and the final model achieves improvements of 1.32 in LAS and 2.65 in BLEU score on these two tasks respectively and it outperforms the greedy structured prediction models in previous literatures.

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Transition-Based Dependency Parsing | State-of-the-Art