Estimating Reference Scopes of Wikipedia Article Inner-links

Wikipedia is the largest online encyclopedia, and utilized as machine-knowledgeable and semantic resources. Links within Wikipedia indicate that two articles or parts of them related about their topics. Existing link detection methods focus on article titles because most of links in Wikipedia point to article titles. But there are a number of links in Wikipedia pointing to corresponding segments, because the whole article is too general and it is hard for readers to obtain the intention of the link. We propose a method to automatically predict whether the link target is a specific segment and provide which segment is most relevant. We propose a combination method of Latent Dirichlet Allocation (LDA) and Maximum Likelihood Estimation (MLE) to represent every segment as a vector, and then we obtain similarity of each segment pair. Finally we utilize variance, standard deviation and other statistical features to predict the results. We also try Word2Vector model to embed all the segments into a semantic space and calculate cosine similarities between segment pairs, then we utilize Random Forest to train a classifier to predict link scopes. Through evaluations on Wikipedia articles, our method achieved reasonable results. Keyword Wikipedia, link suggestion, LDA, word2vector, PMI