This document discusses associating gaze information with human reading strategies. It describes using natural language processing technologies and reading behavior clues like word length and frequency to predict reading strategies, such as fixation and skipping, with 95% similarity to observed reader data. The goal is to better understand general reading strategies regardless of individual differences. It also discusses using a conditional random field model and gaze features to optimize comma placement in text for improved readability.

1. Associating Gaze Information with Human Reading Strategies
Gaze behavior NLP technologies
Reading strategies
Text optimization
i o n s t h r e a t e n i n g t h e i r v e r y e x i s t e n c e ?
● ● ● ●
skipped
・Clues: word surface, POS, word length, frequency, etc.
・Prediction with 0.95 similarity to observed data
(for distribution across readers)
・Regardless of individuality / unstableness
 general reading strategy
: saccade●: fixation
Previous label
: inputSentence The man will have to
: labelFixation
/ skip
:
POS DT
The
Length 0.07
-Trigram
(2,3)
NN
man
0.07
-
(2,3)
MD
will
0.06
0.38
(3,3)
VB
have
0.06
0.48
(3,3)
TO
to
0.08
0.61
(3,3)Screen
-2 -1 0 1 2
:Position
Features of input sequence
:Surprisal
:
:
Word
Optimization of comma-placement
Prediction of word fixations/skips by readers
・For smoothing human reading Linguistic FeaturesCRF model
CRF model-based Comma Predictor
Gaze FeaturesHuman Annotation
Rule-based Comma Filter
+
+
Comma Distribution for Readability
Input (Comma-less) Text