Relation Detection for Indonesian Language using Deep Neural Network -- Support Vector Machine
作者： Ayu Purwarianti , Ramos Janoah Hasudungan
DOI: 10.1109/IALP.2018.8629248
关键词: Embedding 、 Feature (machine learning) 、 Artificial intelligence 、 Classifier (UML) 、 Support vector machine 、 Pattern recognition 、 Word embedding 、 Relation (database) 、 Hyperparameter optimization 、 Artificial neural network 、 Computer science
摘要: Relation Detection is a task to determine whether two entities are related or not. In this paper, we employ neural network do relation detection between named for Indonesian Language. We used feature such as word embedding, position POS-Tag and character embedding. For the model, divide model into parts: Front-part classifier (Convolutional layer LSTM layer) Back-part (Dense SVM). did grid search method of hyper parameter SVM. 6000 sentences training process 1,125 testing. The best result 0.8083 on F1-Score using Convolutional Layer front-part SVM back-part.
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sci-hub.st 参考文章(13)
Sergey Brin, Extracting Patterns and Relations from the World Wide Web Lecture Notes in Computer Science. pp. 172- 183 ,(1999) , 10.1007/10704656_11
Yoon Kim, Convolutional Neural Networks for Sentence Classification empirical methods in natural language processing. pp. 1746- 1751 ,(2014) , 10.3115/V1/D14-1181
Wiwin Suwarningsih, Iping Supriana, Relation extraction with tree kernel for Indonesian sentences 2014 International Conference on ICT For Smart Society (ICISS). pp. 22- 26 ,(2014) , 10.1109/ICTSS.2014.7013147
Nanda Kambhatla, Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations meeting of the association for computational linguistics. pp. 22- ,(2004) , 10.3115/1219044.1219066
Zhou GuoDong, Su Jian, Zhang Jie, Zhang Min, Exploring Various Knowledge in Relation Extraction meeting of the association for computational linguistics. pp. 427- 434 ,(2005) , 10.3115/1219840.1219893
Thien Huu Nguyen, Ralph Grishman, Relation Extraction: Perspective from Convolutional Neural Networks Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing. pp. 39- 48 ,(2015) , 10.3115/V1/W15-1506
Francis C. M. Lau, Chonglin Sun, Zhiyuan Liu, Chunting Zhou, A C-LSTM Neural Network for Text Classification arXiv: Computation and Language. ,(2015)
Naoaki Okazaki, Kentaro Inui, Sho Takase, Fast and Large-scale Unsupervised Relation Extraction pacific asia conference on language, information and computation. pp. 96- 105 ,(2015)
Xipeng Qiu, Xuanjing Huang, Pengfei Liu, Recurrent Neural Network for Text Classification with Multi-Task Learning arXiv: Computation and Language. ,(2016)
Zhenyu Qi, Hongyun Bao, Suncong Zheng, Bo Xu, Jiaming Xu, Peng Zhou, Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling international conference on computational linguistics. pp. 3485- 3495 ,(2016)