Po Hu


Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph
Hanning Gao | Lingfei Wu | Po Hu | Zhihua Wei | Fangli Xu | Bo Long
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods first roughly retrieve the knowledge subgraphs (KSG) that may contain candidate answer, and then search for the exact answer in the KSG. However, the KSG may contain thousands of candidate nodes since the knowledge graph involved in querying is often of large scale, thus decreasing the performance of answer selection. To tackle this problem, we first propose to partition the retrieved KSG to several smaller sub-KSGs via a new subgraph partition algorithm and then present a graph-augmented learning to rank model to select the top-ranked sub-KSGs from them. Our proposed model combines a novel subgraph matching networks to capture global interactions in both question and subgraphs and an Enhanced Bilateral Multi-Perspective Matching model to capture local interactions. Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method. The experimental results on multiple benchmark datasets have demonstrated the effectiveness of our approach.

Event Detection with Dual Relational Graph Attention Networks
Jiaxin Mi | Po Hu | Peng Li
Proceedings of the 29th International Conference on Computational Linguistics

Event detection, which aims to identify instances of specific event types from pieces of text, is a fundamental task in information extraction. Most existing approaches leverage syntactic knowledge with a set of syntactic relations to enhance event detection. However, a side effect of these syntactic-based approaches is that they may confuse different syntactic relations and tend to introduce redundant or noisy information, which may lead to performance degradation. To this end, we propose a simple yet effective model named DualGAT (Dual Relational Graph Attention Networks), which exploits the complementary nature of syntactic and semantic relations to alleviate the problem. Specifically, we first construct a dual relational graph that both aggregates syntactic and semantic relations to the key nodes in the graph, so that event-relevant information can be comprehensively captured from multiple perspectives (i.e., syntactic and semantic views). We then adopt augmented relational graph attention networks to encode the graph and optimize its attention weights by introducing contextual information, which further improves the performance of event detection. Extensive experiments conducted on the standard ACE2005 benchmark dataset indicate that our method significantly outperforms the state-of-the-art methods and verifies the superiority of DualGAT over existing syntactic-based methods.


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)


Context-Enhanced Personalized Social Summarization
Po Hu | Donghong Ji | Chong Teng | Yujing Guo
Proceedings of COLING 2012


Social Summarization via Automatically Discovered Social Context
Po Hu | Cheng Sun | Longfei Wu | Donghong Ji | Chong Teng
Proceedings of 5th International Joint Conference on Natural Language Processing


Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning
Po Hu | Donghong Ji | Hai Wang | Chong Teng
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1


Chinese Text Summarization Based on Thematic Area Detection
Po Hu | Tingting He | Donghong Ji
Text Summarization Branches Out