基于词向量语义分类的微博实体链接方法

冯冲; 石戈; 郭宇航; 龚静; 黄河燕

doi:10.16383/j.aas.2016.c150715

基于词向量语义分类的微博实体链接方法

doi: 10.16383/j.aas.2016.c150715

冯冲^1, ,,
石戈^1,,
郭宇航^1,,
龚静^1,,
黄河燕^1,2,

1.
北京理工大学计算机学院北京 100081
2.
北京市海量语言信息处理与云计算应用工程技术研究中心北京 100081

基金项目:

高等学校博士学科点专项科研基金资助 20121101120026

国家自然科学基金( 61502035

国家重点基础研究发展计划(973计划) 2013CB329303

国家高技术研究发展计划(863计划)( (2015AA015 404

详细信息

作者简介:
石戈北京理工大学计算机学院博士研究生. 主要研究方向为自然语言处理,实体链接, 问答系统. E-mail: tang zhi@126.com

郭宇航北京理工大学计算机学院讲师.2014 年获哈尔滨工业大学计算机科学与技术学院博士学位. 主要研究方向为自然语言处理,信息抽取,机器翻译. E-mail: tang guoyuhang@bit.edu.cn

龚静北京理工大学计算机学院硕士研究生. 主要研究方向为自然语言处理,机器翻译,问答系统. E-mail: tang gongjing@bit.edu.cn

黄河燕北京理工大学计算机学院教授.1989 年获中国科学院计算技术研究所计算机科学与技术博士学位. 主要研究方向为自然语言处理和机器翻译社交网络与信息检索, 智能处理系统. E-mail: tang hhy63@bit.edu.cn

通讯作者:
冯冲北京理工大学计算机学院副研究员. 2005 年获中国科学技术大学计算机科学系博士学位. 主要研究方向为自然语言处理, 信息抽取, 机器翻译. 本文通信作者. E-mail: fengchong@bit.edu.cn

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出版历程
- 收稿日期: 2015-10-29
- 录用日期: 2016-05-03
- 刊出日期: 2016-06-20

An Entity Linking Method for Microblog Based on Semantic Categorization by Word Embeddings

FENG Chong^{1
, ,},
SHI Ge^1
,,
GUO Yu-Hang^1
,,
GONG Jing^1
,,
HUANG He-Yan^{1,2
,}

1.
College of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081
2.
Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing 100081

Funds:

Specialized Research Fund for the Doctoral Program of Higher Education 20121101120026

National Natural Science Foundation of China 61502035

Supported by National Basic Research Program of China (973 Program) 2013CB329303

National High Technology Research and Development Program of China (863 Program) (2015AA015 404

More Information

Author Bio:
SHI Ge Ph. D. candidate at the College of Computer Science and Tech- nology, Beijing Institute of Technology. His research interest covers natural lan- guage processing, entity linking, and question answering system

GUO Yu-Hang Lecturer at the College of Computer Science and Tech- nology, Beijing Institute of Technology. He received his Ph. D. degree from Harbin Institute of Technology in 2014. His research interest covers natural language processing, information extraction, and machine translation

GONG Jing Master student at the College of Computer Science and Tech- nology, Beijing Institute of Technology. Her research interest covers natural lan- guage processing, machine translation, and question an- swering system

HUANG He-Yan Professor at the College of Computer Science and Tech- nology, Beijing Institute of Technology. She received her Ph. D. degree from the Institute of Computing Technology, Chinese Academy of Sciences. Her research interest cov- ers natural language processing, machine translation, social network, information retrieval, and intelligent processing system

Corresponding author: (FENG Chong. Associate professor at the College of Computer Science and Technology, Beijing Institute of Technology. He received his Ph. D. degree from the Department of Computer Sci-ence, University of Science and Technology of China in 2005. His research interest covers natural language pro-cessing, information extraction, and machine translation.Corresponding author of this paper. E-mail:fengchong@bit.edu.cn

摘要

摘要: 微博实体链接是把微博中给定的指称链接到知识库的过程,广泛应用于信息抽取、自动问答等自然语言处理任务(Natural language processing,NLP). 由于微博内容简短,传统长文本实体链接的算法并不能很好地用于微博实体链接任务. 以往研究大都基于实体指称及其上下文构建模型进行消歧,难以识别具有相似词汇和句法特征的候选实体. 本文充分利用指称和候选实体本身所含有的语义信息,提出在词向量层面对任务进行抽象建模,并设计一种基于词向量语义分类的微博实体链接方法. 首先通过神经网络训练词向量模板,然后通过实体聚类获得类别标签作为特征,再通过多分类模型预测目标实体的主题类别来完成实体消歧. 在NLPCC2014公开评测数据集上的实验结果表明,本文方法的准确率和召回率均高于此前已报道的最佳结果,特别是实体链接准确率有显著提升.
- 词向量 /
- 实体链接 /
- 社会媒体处理 /
- 神经网络 /
- 多分类
Abstract: As a widely applied task in natural language processing (NLP), named entity linking (NEL) is to link a given mention to an unambiguous entity in knowledge base. NEL plays an important role in information extraction and question answering. Since contents of microblog are short, traditional algorithms for long texts linking do not fit the microblog linking task well. Precious studies mostly constructed models based on mentions and its context to disambiguate entities, which are difficult to identify candidates with similar lexical and syntactic features. In this paper, we propose a novel NEL method based on semantic categorization through abstracting in terms of word embeddings, which can make full use of semantic involved in mentions and candidates. Initially, we get the word embeddings through neural network and cluster the entities as features. Then, the candidates are disambiguated through predicting the categories of entities by multiple classifiers. Lastly, we test the method on dataset of NLPCC2014, and draw the conclusion that the proposed method gets a better result than the best known work, especially on accurancy.
- Word embedding /
- entity linking /
- social media processing /
- neural network /
- multiple classifiers

HTML全文

图 1 词向量语义分类模型

Fig. 1 Model of semantical categorization by word embeddings

下载: 全尺寸图片幻灯片

图 2 训练数据示例

Fig. 2 Example of the training data

下载: 全尺寸图片幻灯片

图 3 实体链接过程

Fig. 3 Process of entity linking

下载: 全尺寸图片幻灯片

图 4 本文方法在不同参数λ下的F1值

Fig. 4 F1 scores of the combined measure with the λ parameter

下载: 全尺寸图片幻灯片

图 5 SCWE在不同参数k下的F1平均值

Fig. 5 F1 scores of SCWE with the k features

下载: 全尺寸图片幻灯片

表 1 训练集数据统计

Table 1 Statistics in training data

平均每条微博中名词个数	7.91
同一语义类别名词个数超过7的微博	34
同一语义类别名词个数超过6的微博	81
同一语义类别名词个数超过5的微博	207
同一语义类别名词个数超过4的微博	416
同一语义类别名词个数超过3的微博	502

下载: 导出CSV

表 2 同义词表举例

Table 2 Examples of synonym lexicon

文中实体表示(Key)	标准实体表示(Value)
迈克尔乔丹
飞人
篮球之神
迈克尔·杰弗里·乔丹	迈克尔·乔丹
Michael Jordan
Michael Jeffrey Jordan
乔丹

下载: 导出CSV

表 3 歧义词表举例

Table 3 Examples of ambiguity lexicon

标准实体表示(Key)	无歧义真实实体(List)
	苹果(果树)
	苹果(果实)
苹果	苹果(公司)
	苹果(人物)
	苹果(动漫角色)
	苹果(歌曲)

下载: 导出CSV

表 4 实体流行度表举例

Table 4 Examples of entity frequency

无歧义真实实体(Entity)	实体出现次数(Frequency)
苹果(果树)	26
苹果(果实)	39
苹果(公司)	158
苹果(人物)	2

下载: 导出CSV

表 5 实体流行度权值

Table 5 Weights of entity frequency

流行度排行	一	二	三	四	五
权值	1	0.8	0.7	0.6	0.5

下载: 导出CSV

表 6 实验数据规模

Table 6 Scale of experiment data

数据类型	数据规模
同义词表Key总数	4 293 406
同义词表Value总数	1 948 277
歧义词表Key总数	213 764
歧义词表Value总数	2 354 687
实体总数	4 369 348

下载: 导出CSV

表 7 in-KB 实验结果

Table 7 Results of in-KB

系统	准确率	召回率	F1 值
NLPCC	0.7927	0.8488	0.8198
SCWE+EF	0.8137	0.8593	0.8358
EF*	0.7641	0.8142	0.7884
CMEL	0.7951	0.8345	0.8143

下载: 导出CSV

表 8 NIL 实验结果

Table 8 Results of NIL

系统	准确率	召回率	F1 值
NLPCC	0.9024	0.8653	0.8835
SCWE+EF	0.9144	0.8763	0.8949
EF*	0.8871	0.8648	0.8758
CMEL	0.8543	0.8694	0.8461

下载: 导出CSV

表 9 in-KB 实验结果

Table 9 Results of in-KB

λ值	准确率	召回率	F1 值
0	0.7532	0.8016	0.7766
0.2	0.7621	0.8158	0.788
0.4	0.7943	0.8375	0.8153
0.6	0.8137	0.8593	0.8358
0.8	0.8032	0.8432	0.8227
1	0.7983	0.8488	0.8228

下载: 导出CSV

表 10 NIL 实验结果

Table 10 Results of NIL

λ值	准确率	召回率	F1 值
0	0.8432	0.8532	0.8482
0.2	0.8643	0.8713	0.8678
0.4	0.8917	0.8732	0.8824
0.6	0.9148	0.8762	0.8951
0.8	0.9032	0.8754	0.8891
1	0.9013	0.8743	0.8876

下载: 导出CSV

参考文献(21)

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基于词向量语义分类的微博实体链接方法

doi: 10.16383/j.aas.2016.c150715

通讯作者:
冯冲北京理工大学计算机学院副研究员. 2005 年获中国科学技术大学计算机科学系博士学位. 主要研究方向为自然语言处理, 信息抽取, 机器翻译. 本文通信作者. E-mail: fengchong@bit.edu.cn

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An Entity Linking Method for Microblog Based on Semantic Categorization by Word Embeddings

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基于词向量语义分类的微博实体链接方法

doi: 10.16383/j.aas.2016.c150715

通讯作者: 冯冲 北京理工大学计算机学院副研究员. 2005 年获中国科学技术大学计算机科学系博士学位. 主要研究方向为自然语言处理, 信息抽取, 机器翻译. 本文通信作者. E-mail: fengchong@bit.edu.cn

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出版历程

An Entity Linking Method for Microblog Based on Semantic Categorization by Word Embeddings

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出版历程

目录

通讯作者:
冯冲北京理工大学计算机学院副研究员. 2005 年获中国科学技术大学计算机科学系博士学位. 主要研究方向为自然语言处理, 信息抽取, 机器翻译. 本文通信作者. E-mail: fengchong@bit.edu.cn