Hongyun Bao

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: Wang et al. as discussed by the authors explored applying 2D max pooling operation to obtain a fixed-length representation of the text and also utilized 2D convolution to sample more meaningful information of the matrix.

TL;DR: A hybrid neural network model is proposed to extract entities and their relationships without any handcrafted features to achieve the state-of-the-art results on entity and relation extraction task.

TL;DR: A novel tagging scheme is proposed that can convert the joint extraction task to a tagging problem, and different end-to-end models are studied to extract entities and their relations directly, without identifying entities and relations separately.