Combining keyphrase extraction and lexical diversity to characterize ideas in publication titles
This work addresses the need for better tools in bibliometric analysis to track research shifts, but it appears incremental as it builds on existing methods for keyphrase extraction and lexical diversity.
The paper tackled the problem of characterizing the evolution of ideas in scientific papers by analyzing publication titles, proposing to use multiple phrase detection models to generate a more comprehensive set of keyphrases and applying lexical diversity metrics to these sets.
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.