1.2k citations · 2.5k across the 57 of their papers we have counts for
5 papers · 1 filter
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…
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…
Part-of-Speech Relevance Weights for Learning Word Embeddings
Quan Liu, Zhen-Hua Ling, Hui Jiang +1
This paper proposes a model to learn word embeddings with weighted contexts based on part-of-speech (POS) relevance weights. POS is a fundamental element in natural language. Howev…
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…