将名词形容词单独标记为'JN'，提升语法分析准确率。
J2N -- Nominal Adjective Identification and its Application
- 提出将名词形容词独立为'JN'标签，区别于传统词性标注。
- 实验显示该方法在POS标注、分块和指代消解中均有性能提升。
- 适合作为语义解析与句法分析的进阶研究参考。
本文探讨了名词形容词（Nominal Adjectives, NAs）在自然语言处理任务中的挑战，尤其在词性标注（POS tagging）方面。我们提出将NA视为独立词性标签“JN”，并研究其对词性标注、BIO分块及指代消解的影响。实验表明，重新分类NA可提升语法分析与结构理解的准确性。研究采用隐马尔可夫模型（HMM）、最大熵（MaxEnt）模型和Spacy进行验证，并微调BERT模型以识别未标注文本中的NA，证明该方法具有可行性与潜力。
原文摘要 · Abstract (English)
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 finetuned a bert model to identify the NA in untagged text.
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