Papers › J2N -- Nominal Adjective Identification and its Application

J2N -- Nominal Adjective Identification and its Application

22 Sep 2024arXiv:2409.14374archive 2025-07-28

Lemeng Qi, Yang Han, Zhuotong Xie

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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Chunking Coreference Resolution POS POS Tagging Part-Of-Speech Tagging TAG coreference-resolution

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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