Automatically Extracting Personal Name Aliases from the Web

Abstract

Abstract. 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.

Extended E-N-DIST Algorithm for Alias Detection

“…For analysis of intelligence data, a Qualitative Alias Detection was introduced by [30] using Fuzzy Order-of-Magnitude Based on Link Analysis for terrorism-related datasets. Exploration of the name aliases using web mining techniques and the semantic web is presented in [31]. For web and social media, [32] proposed a context-based text mining approach to determine alias names sharing a common name.…”

A survey of graphs in natural language processing

“…Bollegala, Matsuo and Ishizuka (2008) detect aliases based on a word (anchor text) co-occurrence graph in which they compute node rankings, combined using SVMs. The nodes consist of words that appear in anchor texts, which are linked through an edge if the anchor texts in which they appear point to the same URL.…”