Information extraction is the task of finding structured information
from unstructured or semi-structured text. It is an important task in
text mining and has been extensively studied in various research
communities including natural language proc
essing, information
retrieval and Web mining. It has a wide range of applications in
domains such as biomedical literature mining and business
intelligence. Two fundamental tasks of information extraction are
named entity recognition and relation extraction. The former refers to
finding names of entities such as people, organizations and
locations. The latter refers to finding the semantic relations between
entities.
relation extraction systems have made extensive use of features generated
by linguistic analysis modules. Errors in these features lead to errors of
relation detection and classification. In this work, we depart from these
traditional approaches w
ith complicated feature engineering by introducing
a convolutional neural network for relation extraction that automatically
learns features from sentences and minimizes the dependence on external
toolkits and resources. Our model takes advantages of multiple window
sizes for filters and pre-trained word embeddings as an initializer on a nonstatic
architecture to improve the performance.