Estimating reference scopes of wikipedia article inner-links

Wikipedia is the largest online encyclopedia, utilized as machine-knowledgeable and semantic resources. Links within Wikipedia indicate that two linked articles or parts of them are related each other about their topics. Existing link detection methods focus on linking to article titles, because most of links in Wikipedia point to article titles. But there is a number of links in Wikipedia pointing to corresponding specific segments, such as paragraphs, 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 a link target is a specific segment or the whole article, and evaluate 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 produce prediction results. We also apply word embeddings 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. Evaluations on Wikipedia articles show an ensemble of the proposed features achieved the best results.