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Relation Detection And Recognition

Relation Detection And Recognition. *** *** ***. Schema. General Description Name Entity Recognition RDR Training Corpus Generate Relation Detection and Recognition Performance Analysis. General Description-Algorithm. EDR: CRF Character based RDR: SVM Pos is needed.

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Relation Detection And Recognition

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  1. Relation Detection And Recognition *** *** ***

  2. Schema • General Description • Name Entity Recognition • RDR Training Corpus Generate • Relation Detection and Recognition • Performance Analysis

  3. General Description-Algorithm EDR: CRF Character based RDR: SVM Pos is needed

  4. General Description-Workflow

  5. Schema • General Description • Name Entity Recognition • RDR Training Corpus Generate • Relation Detection and Recognition • Performance Analysis

  6. Name Entity Recognition-Algorithm • CRF++ • Character based • Most naive 北 nb 京 ne １ non 月 non １ non 日 non 讯 non 中 nb 华 nm 全 nm 国 nm 总 nm 工 nm 会 ne 今 non 日 non 发 non

  7. Name Entity Recognition-Accuracy • nr precious:100% right:88 error:0 • nt precious:100% right:36 error:0 • ns precious:100% right:56 error:0 • 180/181 • 海湾战争 nz 9 22

  8. Schema • General Description • Name Entity Recognition • RDR Training Corpus Generate • Relation Detection and Recognition • Performance Analysis

  9. RDR Training Corpus Generate • The vector SVM need: e1.type, e2.type,order, dist, w-2,w-1,w0,w1,w2,t-2,t-1,t0,t1,t2, w-2,w-1,w0,w1,w2,t-2,t-1,t0,t1,t2, relation Exp: 国家环保局局长解振华庄重宣布 国家环保局,2,解振华,1,3,11,NULL,NULL,国家环保局,局,长,局,长,解振华,庄,重,null,null,null,NN,NR,NN,NN,null,VA,DEC,E

  10. RDR Training Corpus Generate 1、NLP Pos tag: 国家/NN 环保局/NN 局长/NN 解振华/NR 庄重/VA 宣布：/DEC 2、Compare with Entity: 国家环保局/nt，解振华/nr 3、Find the type front and back null,null,null,NN,NR NN,NN,null,VA,DEC

  11. RDR Training Corpus Generate • 4、Tag the train corpus by hands 国家环保局,2,解振华,1,3,11,NULL,NULL,国家环保局,局,长,局,长,解振华,庄,重,null,null,null,NN,NR,NN,NN,null,VA,DEC,E

  12. RDR Training Corpus Generate • Use Assit Program: Tagged Corpus: 602 sentence 3000+relations

  13. Schema • General Description • Name Entity Recognition • RDR Training Corpus Generate • Relation Detection and Recognition • Performance Analysis

  14. 概述 • 将关系识别问题看作多分类问题 输入：实体对向量集X(x1,x2,……xn) 其中 xi (f1, f2,……fn ) 表示实体对(E1,E2) 输出： xi 所属的类型yi • 使用SVM的方法构造分类器 选取合适的特征集来描述实体对，并映射 到高维实数空间，进行分类

  15. SVM • 支持向量机( Support Vector Machine, SVM) 其主要思想是针对两类分类问题, 在高维空间中寻找一个超平面作为两类的分割, 以保证最小的分类错误率。通过学习, 可以自动寻找那些对分类有较好区分能力的支持向量, 由此构造出的分类器可以最大化类之间的间隔。 • 工具LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) 有可执行的程序来构造多分类器以及训练和预测功能。

  16. 实体对过滤模块 • 关系定义 C:chief(nr-nt) E:employee(nr-nt) L:located in(nt-ns) N:no relation • 实体对过滤模块 将除(nr-nt),(nt-ns)外的实体对过滤，过滤后的实体对作为candidate进行标注(train)或分类(test)

  17. 特征选取和向量化模块 • 选取以下特征构造特征集 e1.type,e2.type,contain,order,dist, w-2,w-1,w1,w2,t-2,t-1,t1,t2, w-2,w-1,w1,w2,t-2,t-1,t1,t2, Relation • 在实际模型训练中有调整 • 映射到向量形式

  18. 向量化模块和scale模块 向量形式 Label index1:value1 …… 1 1:2 2:3 3:4…… Scale（libsvm: svm-scale.exe） 对数据集进行缩放([-1,1]) 便于计算，统一训练集和测试集

  19. 训练模块 • 人工对candidate进行关系标注 • Libsvm: svm-train.exe • 特征集和参数的选择（交叉验证法） • 构造模型

  20. 测试 • SampleTestData • P=76%

  21. Schema • General Description • Name Entity Recognition • RDR Training Corpus Generate • Relation Detection and Recognition • Performance Analysis

  22. 分析与改进 • 前序工作引入的误差 • 训练语料不够大 • 人工标注的语料引入误差 • 特征集的选取（提取语义特征） • 训练参数的选取（网络搜索）

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