Guangyou Zhou


2020

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ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding
Zhiwen Xie | Guangyou Zhou | Jin Liu | Jimmy Xiangji Huang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The goal of Knowledge graph embedding (KGE) is to learn how to represent the low dimensional vectors for entities and relations based on the observed triples. The conventional shallow models are limited to their expressiveness. ConvE (Dettmers et al., 2018) takes advantage of CNN and improves the expressive power with parameter efficient operators by increasing the interactions between head and relation embeddings. However, there is no structural information in the embedding space of ConvE, and the performance is still limited by the number of interactions. The recent KBGAT (Nathani et al., 2019) provides another way to learn embeddings by adaptively utilizing structural information. In this paper, we take the benefits of ConvE and KBGAT together and propose a Relation-aware Inception network with joint local-global structural information for knowledge graph Embedding (ReInceptionE). Specifically, we first explore the Inception network to learn query embedding, which aims to further increase the interactions between head and relation embeddings. Then, we propose to use a relation-aware attention mechanism to enrich the query embedding with the local neighborhood and global entity information. Experimental results on both WN18RR and FB15k-237 datasets demonstrate that ReInceptionE achieves competitive performance compared with state-of-the-art methods.

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基于层次化语义框架的知识库属性映射方法(Property Mapping in Knowledge Base Under the Hierarchical Semantic Framework)
Yu Li (李豫) | Guangyou Zhou (周光有)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

面向知识库的自动问答是自然语言处理的一项重要任务,它旨在对用户提出的自然语言形式问题给出精炼、准确的回复。目前由于缺少数据集、特征不一致等因素,导致难以使用通用的数据和方法实现领域知识库问答。因此,本文将“问题意图”视作不同领域问答可能存在的共同特征,将“问题”与三元组知识库中“关系谓词”的映射过程作为问答核心工作。为了考虑多种层次的语义避免重要信息的损失,本文分别将“基于门控卷积的深层语义”和“基于交互注意力机制的浅层语义”两个方面通过门控感知机制相融合。我们在NLPCC-ICCPOL 2016 KBQA数据集上的实验表明,本文提出的方法与现有的基于CDSSM和BDSSM相比,效能有明显的提升。此外,本文通过构造天文常识知识库,将问题与关系谓词映射模型移植到特定领域,结合Bi-LSTM-CRF模型构建了天文常识自动问答系统。

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基于Graph Transformer的知识库问题生成(Question Generation from Knowledge Base with Graph Transformer)
Yue Hu (胡月) | Guangyou Zhou (周光有)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

知识库问答依靠知识库推断答案需大量带标注信息的问答对,但构建大规模且精准的数据集不仅代价昂贵,还受领域等因素限制。为缓解数据标注问题,面向知识库的问题生成任务引起了研究者关注,该任务是利用知识库三元组自动生成问题。现有方法仅由一个三元组生成的问题简短且缺乏多样性。为生成信息量丰富且多样化的问题,本文采用Graph Transformer和BERT两个编码层来加强三元组多粒度语义表征以获取背景信息。在SimpleQuestions上的实验结果证明了该方法有效性。

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A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment
Zhiwen Xie | Runjie Zhu | Kunsong Zhao | Jin Liu | Guangyou Zhou | Jimmy Xiangji Huang
Proceedings of the 28th International Conference on Computational Linguistics

Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs. In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.

2016

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Bi-Transferring Deep Neural Networks for Domain Adaptation
Guangyou Zhou | Zhiwen Xie | Jimmy Xiangji Huang | Tingting He
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering
Guangyou Zhou | Tingting He | Jun Zhao | Po Hu
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)

2014

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Group Non-negative Matrix Factorization with Natural Categories for Question Retrieval in Community Question Answer Archives
Guangyou Zhou | Yubo Chen | Daojian Zeng | Jun Zhao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Sentiment Classification with Graph Co-Regularization
Guangyou Zhou | Jun Zhao | Daojian Zeng
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Relation Classification via Convolutional Deep Neural Network
Daojian Zeng | Kang Liu | Siwei Lai | Guangyou Zhou | Jun Zhao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Attribute Relation Extraction from Template-inconsistent Semi-structured Text by Leveraging Site-level Knowledge
Yang Liu | Fang Liu | Siwei Lai | Kang Liu | Guangyou Zhou | Jun Zhao
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Statistical Machine Translation Improves Question Retrieval in Community Question Answering via Matrix Factorization
Guangyou Zhou | Fang Liu | Yang Liu | Shizhu He | Jun Zhao
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Joint Inference for Heterogeneous Dependency Parsing
Guangyou Zhou | Jun Zhao
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Exploiting Bilingual Translation for Question Retrieval in Community-Based Question Answering
Guangyou Zhou | Kang Liu | Jun Zhao
Proceedings of COLING 2012

2011

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Improving Dependency Parsing with Fined-Grained Features
Guangyou Zhou | Li Cai | Kang Liu | Jun Zhao
Proceedings of 5th International Joint Conference on Natural Language Processing

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Learning the Latent Topics for Question Retrieval in Community QA
Li Cai | Guangyou Zhou | Kang Liu | Jun Zhao
Proceedings of 5th International Joint Conference on Natural Language Processing

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Phrase-Based Translation Model for Question Retrieval in Community Question Answer Archives
Guangyou Zhou | Li Cai | Jun Zhao | Kang Liu
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Exploiting Web-Derived Selectional Preference to Improve Statistical Dependency Parsing
Guangyou Zhou | Jun Zhao | Kang Liu | Li Cai
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies