文献类型：期刊文献

中文题名：知识驱动的游戏攻略自动标注算法

英文题名：Knowledge driven automatic annotating algorithm for game strategies

作者：陈环环[1];陈小红[2];阮彤[1];高大启[1];王昊奋[1]

机构：[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

年份：2017

卷号：37

期号：1

起止页码：278

中文期刊名：计算机应用

外文期刊名：journal of Computer Applications

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD_E2017_2018】；

基金：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)~~

语种：中文

中文关键词：游戏攻略;知识库;游戏术语;语义标签;决策树

外文关键词：game strategy; knowledge base; game term; semantic tag; decision tree

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。
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.