  In the era of big data, the advancement, improvement, and application of
algorithms in academic research have played an important role in promoting the
development of different disciplines. Academic papers in various disciplines,
especially computer science, contain a large number of algorithms. Identifying
the algorithms from the full-text content of papers can determine popular or
classical algorithms in a specific field and help scholars gain a comprehensive
understanding of the algorithms and even the field. To this end, this article
takes the field of natural language processing (NLP) as an example and
identifies algorithms from academic papers in the field. A dictionary of
algorithms is constructed by manually annotating the contents of papers, and
sentences containing algorithms in the dictionary are extracted through
dictionary-based matching. The number of articles mentioning an algorithm is
used as an indicator to analyze the influence of that algorithm. Our results
reveal the algorithm with the highest influence in NLP papers and show that
classification algorithms represent the largest proportion among the
high-impact algorithms. In addition, the evolution of the influence of
algorithms reflects the changes in research tasks and topics in the field, and
the changes in the influence of different algorithms show different trends. As
a preliminary exploration, this paper conducts an analysis of the impact of
algorithms mentioned in the academic text, and the results can be used as
training data for the automatic extraction of large-scale algorithms in the
future. The methodology in this paper is domain-independent and can be applied
to other domains.
