  Relation extraction (RE) is the task of extracting relations between entities
in text. Most RE methods extract relations from free-form running text and
leave out other rich data sources, such as tables. We explore RE from the
perspective of applying neural methods on tabularly organized data. We
introduce a new model consisting of Convolutional Neural Network (CNN) and
Bidirectional-Long Short Term Memory (BiLSTM) network to encode entities and
learn dependencies among them, respectively. We evaluate our model on a large
and recent dataset and compare results with previous neural methods.
Experimental results show that our model consistently outperforms the previous
model for the task of relation extraction on tabular data. We perform
comprehensive error analyses and ablation study to show the contribution of
various components of our model. Finally, we discuss the usefulness and
trade-offs of our approach, and provide suggestions for fostering further
research.
