基于用户使用行为视角的百度百科词条分类研究

Classifying Baidu Encyclopedia Entries with User Behaviors

【目的】将用户使用行为作为百科词条分类依据,找到并优化具有高使用价值与使用潜力的词条。【方法】结合国内外学者的研究成果,选取用户使用程度与用户认可度作为研究指标,基于波士顿矩阵和BP神经网络方法提出词条分类模型并进行自动分类。【结果】基于用户使用行为指标对词条做出分类并提出相应的发展策略;自动分类方法可以准确判别单一词条所属的词条类别。【局限】对新生词条的研究不足,未考虑丰富度、严谨性等难以准确量化的特征。【结论】拓展百科词条分类的新思路,提出百科词条分类的新方法。

[Objective] This paper classifies Baidu encyclopedia entries based on users’ information behaviors,aiming to identify entries with high potential values.[Methods] We chose the usage and recognition levels as indicators,and proposed a new entry classification model base on Boston matrix and BP neural network.[Results] We classified the Baidu encyclopedia entries automatically with usage indicators and created development strategies for each category.Our new model correctly identified each entry’s category information.[Limitations] More research is needed to study the newly generated entries and features difficult to quantify.[Conclusions] This research proposed an effective method to automatically classify online encyclopedia entries.