document: Mining for personal name aliases on the web We propose a novel approach to find aliases of a given name from the web . We exploit a set of known names and their aliases as training data and extract lexical patterns that convey information related to aliases of names from text snippets returned by a web search engine . The patterns are then used to find candidate aliases of a given name . We use anchor texts and hyperlinks to design a word co-occurrence model and define numerous ranking scores to evaluate the association between a name and its candidate aliases . The proposed method outperforms numerous baselines and previous work on alias extraction on a dataset of personal names , achieving a statistically significant mean reciprocal rank of 0.6718 . Moreover , the aliases extracted using the proposed method improve recall by 20 % in a relation-detection task .
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