WeChat Mini Program
Old Version Features

Morphological Productivity and Neological Intuition

Glossa Psycholinguistics(2024)

Cited 0|Views1
Abstract
This paper investigates the relationship between morphological productivity and neological intuition, defined as the ability to identify novel words as such. It can be hypothesised that the more productive a word-formation process is, the less salient the neologisms it forms will be. We test this hypothesis experimentally on neologisms formed with prefixes and suffixes of variable productivity. Three experiments are conducted, involving lexical identification and reading tasks with eye tracking, to provide a comprehensive description of neological intuition. The negative correlation between productivity and neological salience is supported by experimental results, but only in the case of suffixed neologisms, as opposed to prefixed ones. The effect of affix type on neological intuition can be explained by differences in the grammatical nature of prefixes and suffixes. Broadly speaking, investigating the linguistic factors of neological intuition provides an original approach to both linguistic and psycholinguistic issues related to word structure and lexical processing.
More
Translated text
求助PDF
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined