Suggesting Missing Information in Text Documents

• Main Author: Hodgson, Grant Michael
• Format: Others
• Published: BYU ScholarsArchive 2018
• Subjects: Natural language processing; suggesting; missing; text
• Online Access: https://scholarsarchive.byu.edu/etd/7296; https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=8296&context=etd

A key part of contract drafting involves thinking of issues that have not been addressedand adding language that will address the missing issues. To assist attorneys with this task, we present a pipeline approach for identifying missing information within a contract section. The pipeline takes a contract section as input and includes 1) identifying sections that are similar to the input section from a corpus of contract sections; and 2) identifying and suggesting information from the similar sections that are missing from the input section. By taking advantage of sentence embedding and principal component analysis, this approach suggests sentences that are helpful for finishing a contract. We show that sentence suggestions are more useful than the state of the art topic suggestion algorithm by synthetic experiments and a user study.