Results 31 to 40 of about 6,924 (296)

Valency-Augmented Dependency Parsing [PDF]

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018
We present a complete, automated, and efficient approach for utilizing valency analysis in making dependency parsing decisions. It includes extraction of valency patterns, a probabilistic model for tagging these patterns, and a joint decoding process that explicitly considers the number and types of each token’s syntactic dependents.
Tianze Shi, Lillian Lee
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Improving the performance of graph based dependency parsing by guiding bi-affine layer with augmented global and local features

Intelligent Systems with Applications, 2023
The growing interaction between humans and machines raises the necessity to more sophisticated tools for natural language understanding. Dependency parsing is crucial for capturing the semantics of a sentence.
Mücahit Altıntaş, A. Cüneyd Tantuğ
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Pseudo-projective dependency parsing [PDF]

Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05, 2005
In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures. We show how a data-driven deterministic dependency parser, in itself restricted to projective structures, can be combined with graph transformation techniques to produce non-projective structures.
Joakim Nivre, Jens Nilsson 0001
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Nucleus Composition in Transition-based Dependency Parsing

Computational Linguistics, 2022
Dependency-based approaches to syntactic analysis assume that syntactic structure can be analyzed in terms of binary asymmetric dependency relations holding between elementary syntactic units.
Joakim Nivre, Ali Basirat, Luise Dürlich, Adam Moss   +3 more
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Parsing Chinese Sentences with Grammatical Relations [PDF]

Computational Linguistics, 2019
We report our work on building linguistic resources and data-driven parsers in the grammatical relation (GR) analysis for Mandarin Chinese. Chinese, as an analytic language, encodes grammatical information in a highly configurational rather than ...
Weiwei Sun, Yufei Chen, Xiaojun Wan, Meichun Liu   +3 more
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Global Greedy Dependency Parsing

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
Most syntactic dependency parsing models may fall into one of two categories: transition- and graph-based models. The former models enjoy high inference efficiency with linear time complexity, but they rely on the stacking or re-ranking of partially-built parse trees to build a complete parse tree and are stuck with slower training for the necessity of
Zuchao Li, Hai Zhao 0001, Kevin Parnow
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Combining Syntactic Enhancement with Graph Attention Networks for Aspect-based Sentiment Classification [PDF]

Jisuanji kexue
Aspect-level sentiment classification aims to identify the emotional polarity of a given aspect text.In this field,the combination of graph neural network and syntactic dependency parsing is one of the current hot research directions.Based on the ...
ZHANG Zebao, YU Hannan, WANG Yong, PAN Haiwei
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A pipeline framework for dependency parsing [PDF]

Proceedings of the COLING/ACL on Main conference poster sessions -, 2006
Pipeline computation, in which a task is decomposed into several stages that are solved sequentially, is a common computational strategy in natural language processing. The key problem of this model is that it results in error accumulation and suffers from its inability to correct mistakes in previous stages.
Ming-Wei Chang, Quang Do, Dan Roth 0001
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DPG-LSTM: An Enhanced LSTM Framework for Sentiment Analysis in Social Media Text Based on Dependency Parsing and GCN

Applied Sciences, 2022
Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation.
Zeyu Yin, Jinsong Shao, Muhammad Jawad Hussain, Yajie Hao, Yu Chen, Xuefeng Zhang, Li Wang   +6 more
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Parse Imputation for Dependency Annotations [PDF]

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2015
Syntactic annotation is a hard task, but it can be made easier by allowing annotators flexibility to leave aspects of a sentence underspecified. Unfortunately, partial annotations are not typically directly usable for training parsers. We describe a method for imputing missing dependencies from sentences that have been partially annotated using the ...
Jason Mielens, Liang Sun, Jason Baldridge   +2 more
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