基于词性标记的Source Code标识符标注方法
An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags
摘要：
本文提出了一种集成的源代码标识符词性标注方法。包围标注是一种使用机器学习和来自多个词性标注器的输出来以比词性标注器能够独立获得的更高的质量注释自然语言文本的技术。我们的合奏使用三个国家的最先进的词性标记：SWUM，POSSE和斯坦福大学。我们研究了五种不同类型的标识符名称的合奏的注释的质量：功能，类，属性，参数和声明声明语句在两个单独的单词和完整的标识符名称的水平。同时，我们也研究和讨论了我们的标注器的不足之处，希望通过进一步的研究来促进这些问题的改进。实验结果表明，该集成方法在识别符级的识别率达到75%，在词级的识别率达到84- 86%.这是在标识符级别上从最接近的独立词性标注器增加了+17\%点。
英文摘要：
This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble's annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75\% accuracy at the identifier level and 84-86\% accuracy at the word level. This is an increase of +17\% points at the identifier level from the closest independent part-of-speech tagger.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
