  Form 10-K report is a financial report disclosing the annual financial state
of a public company. It is an important evidence to conduct financial analysis,
i.e., asset pricing, corporate finance. Practitioners and researchers are
constantly designing algorithms to better conduct analysis on information in
the Form 10-K report. The vast majority of previous works focus on quantitative
data. With recent advancement on natural language processing (NLP), textual
data in financial filing attracts more attention. However, to incorporate
textual data for analyzing, Form 10-K Itemization is a necessary pre-process
step. It aims to segment the whole document into several Item sections, where
each Item section focuses on a specific financial aspect of the company. With
the segmented Item sections, NLP techniques can directly apply on those Item
sections related to downstream tasks. In this paper, we develop a Form 10-K
Itemization system which can automatically segment all the Item sections in
10-K documents. The system is both effective and efficient. It reaches a
retrieval rate of 93%.
