Chenguang Wang


2020

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PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction
Yichun Yin | Chenguang Wang | Ming Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction. In this paper, we design the positional dependency-based word embedding (PoD) which considers both dependency context and positional context for aspect term extraction. Specifically, the positional context is modeled via relative position encoding. Besides, we enhance the dependency context by integrating more lexical information (e.g., POS tags) along dependency paths. Experiments on SemEval 2014/2015/2016 datasets show that our approach outperforms other embedding methods in aspect term extraction.

2017

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CROWD-IN-THE-LOOP: A Hybrid Approach for Annotating Semantic Roles
Chenguang Wang | Alan Akbik | Laura Chiticariu | Yunyao Li | Fei Xia | Anbang Xu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Crowdsourcing has proven to be an effective method for generating labeled data for a range of NLP tasks. However, multiple recent attempts of using crowdsourcing to generate gold-labeled training data for semantic role labeling (SRL) reported only modest results, indicating that SRL is perhaps too difficult a task to be effectively crowdsourced. In this paper, we postulate that while producing SRL annotation does require expert involvement in general, a large subset of SRL labeling tasks is in fact appropriate for the crowd. We present a novel workflow in which we employ a classifier to identify difficult annotation tasks and route each task either to experts or crowd workers according to their difficulties. Our experimental evaluation shows that the proposed approach reduces the workload for experts by over two-thirds, and thus significantly reduces the cost of producing SRL annotation at little loss in quality.

2013

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Paraphrasing Adaptation for Web Search Ranking
Chenguang Wang | Nan Duan | Ming Zhou | Ming Zhang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)