A Combined Usage of NLP Libraries Towards Analyzing Software Documents

Abstract

Software documents are commonly processed by natural language processing (NLP) libraries to extract information. The libraries provide similar functional APIs to achieve NLP tasks, numerous toolkits result in a problem of selection. In this work, we propose a method to combine the strengths of different NLP libraries to avoid the subjective selection of a specific NLP library. The combined usage is conducted through two steps, i.e. document-level selection of primary NLP library and sentence-level overwriting. The primary NLP library is determined according to the overlap degree of the results. The highest overlap degree indicated the most effective NLP library on a specific NLP task. Through sentence-level overwriting, the possible fine-gained improvements from other libraries are extracted to overwrite the outputs of primary library. We evaluate the combined method with six widely used NLP libraries and 200 documents from three different sources. The results show that the combined method can generally outperform all the studied NLP libraries in terms of accuracy. The finding means that our combined method can be used instead of individual NLP library for more effective results.