We analysed a dataset of scientific manuscripts that were submitted to various conferences in artificial intelligence. We performed a combination of semantic, lexical and psycholinguistic analyses of the full text of the manuscripts and compared them with the outcome of the peer review process. We found that accepted manuscripts scored lower than rejected manuscripts on two indicators of readability, and that they also used more scientific and artificial intelligence jargon. We also found that accepted manuscripts were written with words that are less frequent, that are acquired at an older age, and that are more abstract than rejected manuscripts. The analysis of references included in the manuscripts revealed that the subset of accepted submissions were more likely to cite the same publications. This finding was echoed by pairwise comparisons of the word content of the manuscripts (i.e. an indicator or semantic similarity), which were more similar in the subset of accepted manuscripts. Finally, we predicted the peer review outcome of manuscripts with their word content, with words related to machine learning and neural networks positively related with acceptance, whereas words related to logic, symbolic processing and knowledge-based systems negatively related with acceptance.

中文翻译：

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人工智能手稿的文本分析揭示了与同行评审结果相关的特征

我们分析了提交给各种人工智能会议的科学手稿数据集。我们对手稿全文进行了语义、词汇和心理语言学分析，并将它们与同行评审过程的结果进行了比较。我们发现被接受的稿件在可读性的两个指标上的得分低于被拒绝的稿件，并且他们还使用了更多的科学和人工智能术语。我们还发现，被接受的手稿使用频率较低、年龄较大时获得的词，并且比被拒绝的手稿更抽象。对手稿中包含的参考文献的分析表明，被接受提交的子集更有可能引用相同的出版物。手稿的单词内容（即指标或语义相似性）的成对比较与这一发现相呼应，它们在被接受手稿的子集中更相似。最后，我们用文字内容预测了手稿的同行评审结果，与机器学习和神经网络相关的词与接受度呈正相关，而与逻辑、符号处理和基于知识的系统相关的词与接受度呈负相关。