%0 Conference Paper %1 xu2015semantic %A Xu, Kun %A Feng, Yansong %A Huang, Songfang %A Zhao, Dongyan %B Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing EMNLP %D 2015 %K classification cnn networks neural relation sampling semantic semeval semeval10 %P 536–540 %T Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling. %U http://dblp.uni-trier.de/db/conf/emnlp/emnlp2015.html#XuFHZ15 %X Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models often suffer from irrelevant information introduced when subjects and objects are in a long distance. In this paper, we propose to learn more robust relation representations from the shortest dependency path through a convolution neural network. We further propose a straightforward negative sampling strategy to improve the assignment of subjects and objects. Experimental results show that our method outperforms the state-of-the-art methods on the SemEval-2010 Task 8 dataset.

@inproceedings{xu2015semantic, abstract = {Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models often suffer from irrelevant information introduced when subjects and objects are in a long distance. In this paper, we propose to learn more robust relation representations from the shortest dependency path through a convolution neural network. We further propose a straightforward negative sampling strategy to improve the assignment of subjects and objects. Experimental results show that our method outperforms the state-of-the-art methods on the SemEval-2010 Task 8 dataset.}, added-at = {2017-10-05T22:37:35.000+0200}, author = {Xu, Kun and Feng, Yansong and Huang, Songfang and Zhao, Dongyan}, biburl = {https://www.bibsonomy.org/bibtex/21240d1169e1ee92205dda5790d1fad99/schwemmlein}, booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing [EMNLP]}, description = {Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling}, interhash = {934f718044adc29d9e293b6d2b7befcd}, intrahash = {1240d1169e1ee92205dda5790d1fad99}, keywords = {classification cnn networks neural relation sampling semantic semeval semeval10}, note = {cite arxiv:1506.07650}, pages = {536–540}, timestamp = {2017-10-06T09:33:40.000+0200}, title = {Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling.}, url = {http://dblp.uni-trier.de/db/conf/emnlp/emnlp2015.html#XuFHZ15}, year = 2015 }