This paper explores the challenges posed by nominal adjectives (NAs) in natural language processing (NLP) tasks, particularly in part-of-speech (POS) tagging. We propose treating NAs as a distinct POS tag, "JN," and investigate its impact on POS tagging, BIO chunking, and coreference resolution. Our study shows that reclassifying NAs can improve the accuracy of syntactic analysis and structural understanding in NLP. We present experimental results using Hidden Markov Models (HMMs), Maximum Entropy (MaxEnt) models, and Spacy, demonstrating the feasibility and potential benefits of this approach. Additionally we trained a bert model to identify the NA in untagged text.

翻译：本文探讨了名词性形容词（NAs）在自然语言处理（NLP）任务中，尤其是在词性（POS）标注方面带来的挑战。我们提出将NAs视为一个独立的词性标签“JN”，并研究其对词性标注、BIO组块和共指消解的影响。我们的研究表明，重新分类NAs可以提高NLP中句法分析和结构理解的准确性。我们展示了使用隐马尔可夫模型（HMMs）、最大熵（MaxEnt）模型以及Spacy的实验结果，证明了该方法的可行性和潜在优势。此外，我们还训练了一个bert模型来识别未标注文本中的NA。