Hu Zhang


Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
Yong Guan | Shaoru Guo | Ru Li | Xiaoli Li | Hu Zhang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. Specifically, we first construct semantic scenario graph and semantic word relation graph based on FrameNet, and subsequently learn their representations and design graph fusion method to enhance their correlation and obtain better semantic representation for summary generation. Experimental results show our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.

A Knowledge-Guided Framework for Frame Identification
Xuefeng Su | Ru Li | Xiaoli Li | Jeff Z. Pan | Hu Zhang | Qinghua Chai | Xiaoqi Han
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using onehot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KGFI significantly outperforms the state-of-the-art methods on two benchmark datasets.


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基于语料库的武侠与仙侠网络小说文体、词汇及主题对比分析(A Corpus-based Contrastive Analysis of Style, Vocabulary and Theme of Wuxia and Xianxia Internet Novels)
Sanle Zhang (张三乐) | Pengyuan Liu (刘鹏远) | Hu Zhang (张虎)
Proceedings of the 19th Chinese National Conference on Computational Linguistics



A Chinese Word Segmentation System Based on Cascade Model
Jianfeng Zhang | Jiaheng Zheng | Hu Zhang | Hongye Tan
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing

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A Study on Consistency Checking Method of Part-Of-Speech Tagging for Chinese Corpora
Hu Zhang | Jiaheng Zheng
International Journal of Computational Linguistics & Chinese Language Processing, Volume 13, Number 2, June 2008


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A Classification-based Algorithm for Consistency Check of Part-of-Speech Tagging for Chinese Corpora
Hu Zhang | Jia-heng Zheng | Ying Zhao
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts