  Extracting biographical information from online documents is a popular
research topic among the information extraction (IE) community. Various natural
language processing (NLP) techniques such as text classification, text
summarisation and relation extraction are commonly used to achieve this. Among
these techniques, RE is the most common since it can be directly used to build
biographical knowledge graphs. RE is usually framed as a supervised machine
learning (ML) problem, where ML models are trained on annotated datasets.
However, there are few annotated datasets for RE since the annotation process
can be costly and time-consuming. To address this, we developed Biographical,
the first semi-supervised dataset for RE. The dataset, which is aimed towards
digital humanities (DH) and historical research, is automatically compiled by
aligning sentences from Wikipedia articles with matching structured data from
sources including Pantheon and Wikidata. By exploiting the structure of
Wikipedia articles and robust named entity recognition (NER), we match
information with relatively high precision in order to compile annotated
relation pairs for ten different relations that are important in the DH domain.
Furthermore, we demonstrate the effectiveness of the dataset by training a
state-of-the-art neural model to classify relation pairs, and evaluate it on a
manually annotated gold standard set. Biographical is primarily aimed at
training neural models for RE within the domain of digital humanities and
history, but as we discuss at the end of this paper, it can be useful for other
purposes as well.
