Beyond bibliometrics, there is interest in characterizing the evolution of
the number of ideas in scientific papers. A common approach for investigating
this involves analyzing the titles of publications to detect vocabulary changes
over time. With the notion that phrases, or more specifically keyphrases,
represent concepts, lexical diversity metrics are applied to phrased versions
of the titles. Thus changes in lexical diversity are treated as indicators of
shifts, and possibly expansion, of research. Therefore, optimizing detection of
keyphrases is an important aspect of this process. Rather than just one, we
propose to use multiple phrase detection models with the goal to produce a more
comprehensive set of keyphrases from the source corpora. Another potential
advantage to this approach is that the union and difference of these sets may
provide automated techniques for identifying and omitting non-specific phrases.
We compare the performance of several phrase detection models, analyze the
keyphrase sets output of each, and calculate lexical diversity of corpora
variants incorporating keyphrases from each model, using four common lexical
diversity metrics.

本文研究了利用不同短语检测模型检测科学论文的标题从而找出涵盖科研进展的关键词组，目的在于优化文献词汇多样性的度量与自动化剔除非特定短语。研究结果比较了多个短语检测模型的性能，并分析了每个模型生成的关键词组，使用四个常用的词汇多样性指标计算了每个模型关键词组的文献变量中的词汇多样性。