Home
Catalogue search
Refine your search:
Keyword
Creator / Publisher:
Hattori, Koosuke (3)
Hoguro, Masahiro (3)
Sagara, Rikunari (3)
Taguchi, Ryo (3)
Taniguchi, Akira (3)
Taniguchi, Tadahiro (3)
Umezaki, Taizo (3)
Year:
2021 (3)
Medium
Type
BLLDB-Access
Search in the Catalogues and Directories
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
Sort by
creator [A → Z]
'
creator [Z → A]
'
publishing year ↑ (asc)
'
publishing year ↓ (desc)
'
title [A → Z]
'
title [Z → A]
'
Simple Search
Hits 1 – 3 of 3
1
Unsupervised lexical acquisition of relative spatial concepts using spoken user utterances ...
Sagara, Rikunari
;
Taguchi, Ryo
;
Taniguchi, Akira
. - : Taylor & Francis, 2021
BASE
Show details
2
Unsupervised lexical acquisition of relative spatial concepts using spoken user utterances ...
Sagara, Rikunari
;
Taguchi, Ryo
;
Taniguchi, Akira
. - : Taylor & Francis, 2021
BASE
Show details
3
Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances ...
Sagara, Rikunari
;
Taguchi, Ryo
;
Taniguchi, Akira
;
Taniguchi, Tadahiro
;
Hattori, Koosuke
;
Hoguro, Masahiro
;
Umezaki, Taizo
. - : arXiv, 2021
Abstract:
This paper proposes methods for unsupervised lexical acquisition for relative spatial concepts using spoken user utterances. A robot with a flexible spoken dialog system must be able to acquire linguistic representation and its meaning specific to an environment through interactions with humans as children do. Specifically, relative spatial concepts (e.g., front and right) are widely used in our daily lives, however, it is not obvious which object is a reference object when a robot learns relative spatial concepts. Therefore, we propose methods by which a robot without prior knowledge of words can learn relative spatial concepts. The methods are formulated using a probabilistic model to estimate the proper reference objects and distributions representing concepts simultaneously. The experimental results show that relative spatial concepts and a phoneme sequence representing each concept can be learned under the condition that the robot does not know which located object is the reference object. Additionally, ... : 27 pages, 12 figures, submitted to Advanced Robotics ...
Keyword:
Artificial Intelligence cs.AI
;
FOS Computer and information sciences
;
I.2.9
;
Robotics cs.RO
URL:
https://arxiv.org/abs/2106.08574
https://dx.doi.org/10.48550/arxiv.2106.08574
BASE
Hide details
Mobile view
All
Catalogues
UB Frankfurt Linguistik
0
IDS Mannheim
0
OLC Linguistik
0
UB Frankfurt Retrokatalog
0
DNB Subject Category Language
0
Institut für Empirische Sprachwissenschaft
0
Leibniz-Centre General Linguistics (ZAS)
0
Bibliographies
BLLDB
0
BDSL
0
IDS Bibliografie zur deutschen Grammatik
0
IDS Bibliografie zur Gesprächsforschung
0
IDS Konnektoren im Deutschen
0
IDS Präpositionen im Deutschen
0
IDS OBELEX meta
0
MPI-SHH Linguistics Collection
0
MPI for Psycholinguistics
0
Linked Open Data catalogues
Annohub
0
Online resources
Link directory
0
Journal directory
0
Database directory
0
Dictionary directory
0
Open access documents
BASE
3
Linguistik-Repository
0
IDS Publikationsserver
0
Online dissertations
0
Language Description Heritage
0
© 2013 - 2024 Lin|gu|is|tik
|
Imprint
|
Privacy Policy
|
Datenschutzeinstellungen ändern