  Literature analysis facilitates researchers to acquire a good understanding
of the development of science and technology. The traditional literature
analysis focuses largely on the literature metadata such as topics, authors,
abstracts, keywords, references, etc., and little attention was paid to the
main content of papers. In many scientific domains such as science, computing,
engineering, etc., the methods and datasets involved in the scientific papers
published in those domains carry important information and are quite useful for
domain analysis as well as algorithm and dataset recommendation. In this paper,
we propose a novel entity recognition model, called MDER, which is able to
effectively extract the method and dataset entities from the main textual
content of scientific papers. The model utilizes rule embedding and adopts a
parallel structure of CNN and Bi-LSTM with the self-attention mechanism. We
evaluate the proposed model on datasets which are constructed from the
published papers of four research areas in computer science, i.e., NLP, CV,
Data Mining and AI. The experimental results demonstrate that our model
performs well in all the four areas and it features a good learning capacity
for cross-area learning and recognition. We also conduct experiments to
evaluate the effectiveness of different building modules within our model which
indicate that the importance of different building modules in collectively
contributing to the good entity recognition performance as a whole. The data
augmentation experiments on our model demonstrated that data augmentation
positively contributes to model training, making our model much more robust in
dealing with the scenarios where only small number of training samples are
available. We finally apply our model on PAKDD papers published from 2009-2019
to mine insightful results from scientific papers published in a longer time
span.
