  Future work sentences (FWS) are the particular sentences in academic papers
that contain the author's description of their proposed follow-up research
direction. This paper presents methods to automatically extract FWS from
academic papers and classify them according to the different future directions
embodied in the paper's content. FWS recognition methods will enable subsequent
researchers to locate future work sentences more accurately and quickly and
reduce the time and cost of acquiring the corpus. The current work on automatic
identification of future work sentences is relatively small, and the existing
research cannot accurately identify FWS from academic papers, and thus cannot
conduct data mining on a large scale. Furthermore, there are many aspects to
the content of future work, and the subdivision of the content is conducive to
the analysis of specific development directions. In this paper, Nature Language
Processing (NLP) is used as a case study, and FWS are extracted from academic
papers and classified into different types. We manually build an annotated
corpus with six different types of FWS. Then, automatic recognition and
classification of FWS are implemented using machine learning models, and the
performance of these models is compared based on the evaluation metrics. The
results show that the Bernoulli Bayesian model has the best performance in the
automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT
model has the best performance in the automatic classification task, with the
weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and
gain a deep understanding of the key content described in FWS, and we also
demonstrate that content determination in FWS will be reflected in the
subsequent research work by measuring the similarity between future work
sentences and the abstracts.
