Automatically Extracting Personal Name Aliases from the Web

Extracting aliases of an entity is important for various tasks such as identification of relations among entities, web search and entity disambiguation. To extract relations among entities properly, one must first identify those entities. We propose a novel approach to find aliases of a given name using automatically extracted lexical patterns. 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 to design a word co-occurrence model and use it to define various ranking scores to measure the association between a name and a candidate alias. The ranking scores are integrated with page-count-based association measures using support vector machines to leverage a robust alias detection method. 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. Experiments carried out using a dataset of location names and Japanese personal names suggest the possibility of extending the proposed method to extract aliases for different types of named entities and for other languages. Moreover, the aliases extracted using the proposed method improve recall by 20% in a relation-detection task.