Bo Xu

TL;DR: The experimental results on the SemEval-2010 relation classification task show that the AttBLSTM method outperforms most of the existing methods, with only word vectors.

TL;DR: The Speech-Transformer is presented, a no-recurrence sequence-to-sequence model entirely relies on attention mechanisms to learn the positional dependencies, which can be trained faster with more efficiency and a 2D-Attention mechanism which can jointly attend to the time and frequency axes of the 2-dimensional speech inputs, thus providing more expressive representations for the Speech- Transformer.

Abstract: Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-toend models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What’s more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.

TL;DR: One of the proposed models achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks and also utilizes 2D convolution to sample more meaningful information of the matrix.

TL;DR: A unified framework to expand short texts based on word embedding clustering and convolutional neural network and semantic cliques via fast clustering is proposed, which validates the effectiveness of the proposed method on two open benchmarks.