Corpus ID: 400148

Syntax Aware LSTM Model for Chinese Semantic Role Labeling

@article{Qian2017SyntaxAL,
  title={Syntax Aware LSTM Model for Chinese Semantic Role Labeling},
  author={Feng Qian and Lei Sha and Baobao Chang and Luochen Liu and Ming Zhang},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.00405}
}
As for semantic role labeling (SRL) task, when it comes to utilizing parsing information, both traditional methods and recent recurrent neural network (RNN) based methods use the feature engineering way. In this paper, we propose Syntax Aware Long Short Time Memory(SA-LSTM). The structure of SA-LSTM modifies according to dependency parsing information in order to model parsing information directly in an architecture engineering way instead of feature engineering way. We experimentally… Expand
Neural Models for Reasoning over Multiple Mentions Using Coreference
Many problems in NLP require aggregating information from multiple mentions of the same entity which may be far apart in the text. Existing Recurrent Neural Network (RNN) layers are biased towardsExpand
EZLDA: Efficient and Scalable LDA on GPUs
TLDR
EZLDA is introduced which achieves efficient and scalable LDA training on GPUs with the following three contributions: three-branch sampling method which takes advantage of the convergence heterogeneity of various tokens to reduce the redundant sampling task, and a hierarchical workload balancing solution to address the extremely skewed workload imbalance problem. Expand

References

SHOWING 1-10 OF 25 REFERENCES
Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks
TLDR
Bidirectional recurrent neural network with long-short-term memory (LSTM) with RNN to capture bidirectional and long-range dependencies in a sentence with minimal feature engineering is introduced. Expand
End-to-end learning of semantic role labeling using recurrent neural networks
TLDR
This work proposes to use deep bi-directional recurrent network as an end-to-end system for SRL, which takes only original text information as input feature, without using any syntactic knowledge. Expand
Chinese Semantic Role Labeling with Shallow Parsing
TLDR
This paper evaluates SRL methods that take partial parses as inputs and implements SRL systems which cast SRL as the classification of syntactic chunks with IOB2 representation for semantic roles (i.e. semantic chunks). Expand
Improving Chinese Semantic Role Labeling with English Proposition Bank
TLDR
This paper introduces a two-pass approach to do Chinese SRL with a Recurrent Neural Network (RNN) model and uses English Proposition Bank (EPB) to improve the performance ofChinese SRL. Expand
Improving Chinese Semantic Role Labeling with Rich Syntactic Features
TLDR
This work empirically analyze the two-fold effect, grouping words into constituents and providing syntactic information, and proposes a set of additional features, some of which are designed to better capture structural information in Chinese SRL. Expand
Improving Chinese Semantic Role Classification with Hierarchical Feature Selection Strategy
TLDR
A semantic role classifier based on a hierarchical feature selection strategy that outperforms the strong baseline significantly and is also the state-of-the-art on Chinese SRC is built. Expand
Neural Semantic Role Labeling with Dependency Path Embeddings
TLDR
A novel model for semantic role labeling that makes use of neural sequence modeling techniques and treats complex syntactic structures and related phenomena, such as nested subordinations and nominal predicates, as subsequences of lexicalized dependency paths and learns suitable embedding representations. Expand
Multi-Predicate Semantic Role Labeling
TLDR
It is proved that different predicates in a sentence could help each other during SRL, and a discriminative reranking approach to perform role classification of the shared arguments, in which a large set of global features are proposed. Expand
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
TLDR
It is shown that full syntactic parsing information is, by far, most relevant in identifying the argument, especially in the very first stagethe pruning stage, and an effective and simple approach of combining different semantic role labeling systems through joint inference is proposed, which significantly improves its performance. Expand
Word Based Chinese Semantic Role Labeling with Semantic Chunking
TLDR
The experiments have shown that the semantic chunking based method outperforms previously best-reported results on Chinese SRL, if the word segmentation and part-of-speech (POS) tagging are both correct. Expand
...
1
2
3
...