Genealogy Extraction and Tree Generation from Free Form Text

<p>Genealogical records play a crucial role in helping people to discover their lineage and to understand where they come from. They provide a way for people to celebrate their heritage and to possibly reconnect with family they had never considered. However, genealogical records are hard to come by for ordinary people since their information is not always well established in known databases. There often is free form text that describes a person’s life, but this must be manually read in order to extract the relevant genealogical information. In addition, multiple texts may have to be read in order to create an extensive tree. This thesis proposes a novel three part system which can automatically interpret free form text to extract relationships and produce a family tree compliant with GED- COM formatting. The first subsystem builds an extendable database of genealogical records that are systematically extracted from free form text. This corpus provides the tagged data for the second subsystem, which trains a Naı̈ve Bayes classifier to predict relationships from free form text by examining the types of relationships for pairs of entities and their associated feature vectors. The last subsystem accumulates extracted relationships into family trees. When a multiclass Naı̈ve Bayes classifier is used, the proposed system achieves an accuracy of 54%. When binary Naı̈ve Bayes classifiers are used, the proposed system achieves accuracies of 69% for the child to parent relationship classifier, 75% for the spousal relationship classifier, and 73% for the sibling relationship classifier.</p>

Name thesis:degree_name

MS in Computer Science

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2017

Author dc:creator

• Chu, Timothy Sui-Tim

Contributors dc:contributor

• Foaad Khosmood

• Genealogy Extraction
• Relation Extraction
• Information Extraction
• Wikipedia
• Machine Learning
• Natural Language Processing
• Computer Engineering

Identifier

10.15368/theses.2017.112

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3086