Genealogy research is the study of family history using available resources such as historical records. Ancestry provides its customers with one of the world's largest online genealogical index with billions of records from a wide range of sources, including vital records such as birth and death certificates, census records, court and probate records among many others. Search at Ancestry aims to return relevant records from various record types, allowing our subscribers to build their family trees, research their family history, and make meaningful discoveries about their ancestors from diverse perspectives.

At Ancestry, we apply learning to rank algorithms to a new area to assist our customers in better understanding their family history. The foundation of our service is an extensive and unique collection of billions of historical records that we have digitized and indexed. Currently, our content collection includes 20 billion historical records. The record data consists of birth records, death records, marriage records, adoption records, census records, obituary records, among many others types. It is important for us to return relevant records from diversified record types in order to assist our customers to better understand their family history.

This paper presents a formal analysis for a large class of words called alternative markers, which includes other(than), such(as), and besides. These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them. I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine's operational semantics. The value of this approach is that as the operational semantics of natural language applications improve, even larger improvements are possible.