8,093 research outputs found
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
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-to-end 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
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models
The recent work of Super Characters method using two-dimensional word
embedding achieved state-of-the-art results in text classification tasks,
showcasing the promise of this new approach. This paper borrows the idea of
Super Characters method and two-dimensional embedding, and proposes a method of
generating conversational response for open domain dialogues. The experimental
results on a public dataset shows that the proposed SuperChat method generates
high quality responses. An interactive demo is ready to show at the workshop.Comment: 5 pages, 2 figures, 1 table. Accepted by CVPR2019 Language and Vision
Worksho
Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds
A novel multi-scale operator for unorganized 3D point clouds is introduced.
The Difference of Normals (DoN) provides a computationally efficient,
multi-scale approach to processing large unorganized 3D point clouds. The
application of DoN in the multi-scale filtering of two different real-world
outdoor urban LIDAR scene datasets is quantitatively and qualitatively
demonstrated. In both datasets the DoN operator is shown to segment large 3D
point clouds into scale-salient clusters, such as cars, people, and lamp posts
towards applications in semi-automatic annotation, and as a pre-processing step
in automatic object recognition. The application of the operator to
segmentation is evaluated on a large public dataset of outdoor LIDAR scenes
with ground truth annotations.Comment: To be published in proceedings of 3DIMPVT 201
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