知识驱动的游戏攻略自动标注算法

doi:10.11772/j.issn.1001-9081.2017.01.0278

计算机应用 ›› 2017, Vol. 37 ›› Issue (1): 278-283.DOI: 10.11772/j.issn.1001-9081.2017.01.0278

• 应用前沿、交叉与综合 • 上一篇下一篇

知识驱动的游戏攻略自动标注算法

陈环环¹, 陈小红², 阮彤¹, 高大启¹, 王昊奋¹

1. 华东理工大学计算机科学与工程系, 上海 200237;
2. 盛趣信息技术(上海)有限公司, 上海 201203

收稿日期:2016-08-18 修回日期:2016-09-06 出版日期:2017-01-10 发布日期:2017-01-09
通讯作者: 陈环环
作者简介:陈环环(1990-),女,山东菏泽人,硕士研究生,主要研究方向:数据挖掘、自然语言处理;陈小红(1974-),男,浙江永康人,主要研究方向:大数据、虚拟世界的用户行为;阮彤(1973-),女,江苏扬州人,教授,博士,CCF会员,主要研究方向:自然语言处理、信息抽取、数据质量;高大启(1957-),男,湖北宜昌人,教授,博士,主要研究方向:模式识别、机器学习;王昊奋(1982-),男,上海人,讲师,博士,CCF会员,主要研究方向:知识图谱、图数据库、数据挖掘。
基金资助:
国家自然科学基金资助项目（61402173）；上海经信委“软件集成电路产业发展专项资金”项目（140304）。

Knowledge driven automatic annotating algorithm for game strategies

CHEN Huanhuan¹, CHEN Xiaohong², RUAN Tong¹, GAO Daqi¹, WANG Haofen¹

1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;
2. Shengqu Information Technology(Shanghai) Company Limited, Shanghai 201203, China

Received:2016-08-18 Revised:2016-09-06 Online:2017-01-10 Published:2017-01-09
Supported by:
This work is partially supported by the National Science Foundation of China (61402173), the Software and Integrated Circuit Industry Development Special Funds of Shanghai Economy and Information Technology Commission (140304).

摘要/Abstract

摘要： 为了帮助用户快速检索感兴趣的游戏攻略，提出了知识驱动的游戏攻略自动标注算法。首先，对每款游戏的多个资讯网站进行融合，自动构建游戏领域知识库；然后，再通过游戏领域词汇发现算法和决策树分类模型，抽取游戏攻略中的游戏术语；由于游戏术语在攻略中大多以简称的形式存在，故最后将攻略中游戏术语和知识库进行链接得到该术语所对应的全称即语义标签对攻略进行标注。在多款游戏上的实验结果表明，所提出的游戏攻略标注方法的准确率高达90%。同时，游戏领域词汇发现算法与其他术语抽取方法n-gram语言模型相比取得了更好的效果。

关键词: 游戏攻略, 知识库, 游戏术语, 语义标签, 决策树

Abstract: To help users to quickly retrieve the interesting game strategies, a knowledge driven automatic annotating algorithm for game strategies was proposed. In the proposed algorithm, the game domain knowledge base was built automatically by fusing multiple sites that provide information for each game. By using the game domain vocabulary discovering algorithm and decision tree classification model, game terms of the game strategies were extracted. Since most terms existing in the strategies in the form of abbreviation, the game terms were finally linked to knowledge base to generate the full name semantic tags for them. The experimental results on many games show that the precision of the proposed game strategy annotating method is as high as 90%. Moreover, the game domain vocabulary discovering algorithm has a better result compared with the n-gram language model.

Key words: game strategy, knowledge base, game term, semantic tag, decision tree

中图分类号:

TP391.1

陈环环, 陈小红, 阮彤, 高大启, 王昊奋. 知识驱动的游戏攻略自动标注算法[J]. 计算机应用, 2017, 37(1): 278-283.

CHEN Huanhuan, CHEN Xiaohong, RUAN Tong, GAO Daqi, WANG Haofen. Knowledge driven automatic annotating algorithm for game strategies[J]. Journal of Computer Applications, 2017, 37(1): 278-283.

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知识驱动的游戏攻略自动标注算法

Knowledge driven automatic annotating algorithm for game strategies

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