Naming practices offer a window onto linguistic processes of productivity that rely on input from interacting streams of information. Previous studies have looked at proper personal names and binomial combinations of proper personal names to show that phonological features such as rhythm, semantic features such as gender, and corpus features such as word frequency play an important role in nami...

Name entities (NEs), especially personal names, are very important components in interpreting some kinds of text documents e.g. news. To extract personal names efficiently, statistical language models are required to denote characteristics of personal names. Among these characteristics, nationality of a name is a useful source for interpreting the text document. Automatically inferencing nation...

We propose a new statistical approach to extracting personal names from a corpus. One of the key points of our approach is that it can both automatically learn the characteristics of personal names from a large training corpus and make good use of human empirical knowledge (e.g., Context Free Grammar). Furthermore, our approach also assigns confidence measures to the extracted personal names, c...

Personal names are important and common information in many data sources, ranging from social networks and news articles to patient records and scientific documents. They are often used as queries for retrieving records and also as key information for linking documents from multiple sources. Matching personal names can be challenging due to variations in spelling and various formatting of names...