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Main Authors: Qi, Lemeng, Han, Yang, Xie, Zhuotong
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2409.14374
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author Qi, Lemeng
Han, Yang
Xie, Zhuotong
author_facet Qi, Lemeng
Han, Yang
Xie, Zhuotong
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2409_14374
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle J2N -- Nominal Adjective Identification and its Application
Qi, Lemeng
Han, Yang
Xie, Zhuotong
Computation and Language
I.2.7
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.
title J2N -- Nominal Adjective Identification and its Application
topic Computation and Language
I.2.7
url https://arxiv.org/abs/2409.14374