The paper argues that token-level language models should be (approximately) marginalized into character-level language models before they are used in psycholinguistic studies to compute the surprisal of a region of interest; then, the marginalized character-level language model can be used to compute the surprisal of ...
Oct 3, 2024
November 12-16, 2024 ©2024 Association for Computational Linguistics. On␣the␣Proper␣Treatment␣of␣Tokenization␣in␣Psycholinguistics*. Mario Giulianelli.
Oct 3, 2024 · On the Proper Treatment of Tokenization in Psycholinguistics. October ... psycholinguists. should properly apply token-level language models.
Oct 31, 2024 · Language models are widely used in com- putational psycholinguistics to test theories that relate the negative log probability (the.
Nov 30, 2024 · Language models are widely used in computational psycholinguistics to test theories that relate the negative log probability (the surprisal) of ...
Oct 31, 2024 · The paper presents a novel approach to address the challenges of using modern language models in psycholinguistic studies.
Oct 12, 2024 · Explore the top 10 tokenization techniques essential for NLP tasks, from simple whitespace methods to complex subword strategies.
People also ask
What are the techniques of psycholinguistics?
How do we process language in psycholinguistics?
What is word processing in psycholinguistics?
Jun 7, 2024 · Tokenization is the process of splitting a sentence or document into smaller units, known as tokens, which can be processed by models.
Jan 31, 2024 · Tokenization is the process of dividing a text into smaller units known as tokens. Tokens are typically words or sub-words in the context of natural language ...
Tokenization in NLP is the process of dividing text into smaller units called tokens for easier analysis and processing.