1.2k citations · 2k across the 17 of their papers we have counts for
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Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge
Quan Liu, Hui Jiang, Zhen-Hua Ling +3
In this paper, we propose commonsense knowledge enhanced embeddings (KEE) for solving the Pronoun Disambiguation Problems (PDP). The PDP task we investigate in this paper is a comp…
Neural Networks Models for Entity Discovery and Linking
Dan Liu, Wei Lin, Shiliang Zhang +2
This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have bui…
Distraction-Based Neural Networks for Document Summarization
Qian Chen, Xiaodan Zhu, Zhenhua Ling +2
Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sent…
Enhanced LSTM for Natural Language Inference
Qian Chen, Xiaodan Zhu, Zhenhua Ling +3
Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (B…
Attention-over-Attention Neural Networks for Reading Comprehension
Yiming Cui, Zhipeng Chen, Si Wei +3
Cloze-style queries are representative problems in reading comprehension. Over the past few months, we have seen much progress that utilizing neural network approach to solve Cloze…
Probabilistic Reasoning via Deep Learning: Neural Association Models
Quan Liu, Hui Jiang, Andrew Evdokimov +4
In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence. We propose to use neural netw…