  With the enrichment of literature resources, researchers are facing the
growing problem of information explosion and knowledge overload. To help
scholars retrieve literature and acquire knowledge successfully, clarifying the
semantic structure of the content in academic literature has become the
essential research question. In the research on identifying the structure
function of chapters in academic articles, only a few studies used the deep
learning model and explored the optimization for feature input. This limits the
application, optimization potential of deep learning models for the research
task. This paper took articles of the ACL conference as the corpus. We employ
the traditional machine learning models and deep learning models to construct
the classifiers based on various feature input. Experimental results show that
(1) Compared with the chapter content, the chapter title is more conducive to
identifying the structure function of academic articles. (2) Relative position
is a valuable feature for building traditional models. (3) Inspired by (2),
this paper further introduces contextual information into the deep learning
models and achieved significant results. Meanwhile, our models show good
migration ability in the open test containing 200 sampled non-training samples.
We also annotated the ACL main conference papers in recent five years based on
the best practice performing models and performed a time series analysis of the
overall corpus. This work explores and summarizes the practical features and
models for this task through multiple comparative experiments and provides a
reference for related text classification tasks. Finally, we indicate the
limitations and shortcomings of the current model and the direction of further
optimization.
