TY - JOUR
T1 - Unsupervised Learning Enables Extraction of Tactile Information from Text Database
AU - Nagatomo, Tatsuho
AU - Hiraki, Takefumi
AU - Ishizuka, Hiroki
AU - Miki, Norihisa
N1 - Publisher Copyright:
Author
PY - 2021
Y1 - 2021
N2 - In this work, we propose a new approach to tactile research using natural language processing of archival word corpus as the database. Tactile perception, or assessment of surfaces, is recognized as a language. Thus, by extracting touch-related words and sentences from a text corpus and learning their relationships, we can ultimately learn how humans perceive surfaces. We selected 6 adjectives and 42 onomatopoeias in Japanese as our tactile words. The adjectives represent physical properties, such as roughness and hardness, while onomatopoeias, such as “zara-zara” and “tsuru-tsuru,” are widely used to describe surfaces in Japanese and can correspond to both physical texture cognition and affective cognition. First, using natural language processing of word corpora, we successfully mapped the onomatopoeias with respect to the 6 adjectives, which matched well with the results based on an enquete-based survey. This verified the effectiveness of natural language processing for tactile research. In addition, principal component analysis revealed new tactile dimensions based on onomatopoeias, which we presumably assessaffective tactile dimensions. The proposed approach using natural language processing of archival text databases can provide a large number of datasets for tactile research and culminate in new findings and insights.
AB - In this work, we propose a new approach to tactile research using natural language processing of archival word corpus as the database. Tactile perception, or assessment of surfaces, is recognized as a language. Thus, by extracting touch-related words and sentences from a text corpus and learning their relationships, we can ultimately learn how humans perceive surfaces. We selected 6 adjectives and 42 onomatopoeias in Japanese as our tactile words. The adjectives represent physical properties, such as roughness and hardness, while onomatopoeias, such as “zara-zara” and “tsuru-tsuru,” are widely used to describe surfaces in Japanese and can correspond to both physical texture cognition and affective cognition. First, using natural language processing of word corpora, we successfully mapped the onomatopoeias with respect to the 6 adjectives, which matched well with the results based on an enquete-based survey. This verified the effectiveness of natural language processing for tactile research. In addition, principal component analysis revealed new tactile dimensions based on onomatopoeias, which we presumably assessaffective tactile dimensions. The proposed approach using natural language processing of archival text databases can provide a large number of datasets for tactile research and culminate in new findings and insights.
KW - Bit error rate
KW - Cognition
KW - Computational modeling
KW - Friction
KW - Machine Learning
KW - Natural Language Processing
KW - Natural language processing
KW - Onomatopoeia
KW - Rough surfaces
KW - Surface roughness
KW - Tactile Perception
KW - Unsupervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85120078041&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120078041&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3130277
DO - 10.1109/ACCESS.2021.3130277
M3 - Article
AN - SCOPUS:85120078041
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -