Uses of facial expressions of android head system according to gender and age

Ahn, Ho Seok; Lee, Dong‐Wook; Choi, Dongwoon; Lee, Duk-Yeon; Hur, Manhong; Lee, Ho-Gil

doi:10.1109/icsmc.2012.6378084

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…We exploited a public dataset from a tweet-sentiment-extraction competition 8 . The organiser provided 27k training samples composed of raw tweet sentence, selected text, and sentiment, as shown in Table 4.…”

Section: Exploratory Data Analysis (Eda)mentioning

confidence: 99%

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

Lim,

Sa,

Ahn

et al. 2021

Preprint

Self Cite

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Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience and computer science. This stems from its high-degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.

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Section: Exploratory Data Analysis (Eda)mentioning

confidence: 99%

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

Lim,

Sa,

Ahn

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Both the television and the tablet were used for presenting math quizzes. The robot platform is a real human-sized female-type android robot called EveR-4, which has 30 degrees of freedom in the face for expressing natural emotions [1]. The robot interacted with verbal and non-verbal modalities.…”

Section: Interaction and System Designmentioning

confidence: 99%

Building an automated engagement recognizer based on video analysis

Jang

Park

Yang

et al. 2014

Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction

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This paper presents a process to build a classifier in a datadriven way for recognizing engagement of children in a robotbased math quiz game. The process consists of collecting video recordings from HRI experiments; annotating the social signals and engagement states via video analysis; extracting feature vectors from the annotations and training classifiers. We conducted an experiment with 7 participants of 10 ∼ 11 years of age using an android robot EveR-4. With three coders annotating the video recordings and extracting features by snapshot model with 1-second time window, we achieved 84.83% recall performance.

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“…This is because text often contains limited information to describe emotions sufficiently. It also differs across gender, age, and cultural backgrounds [8]. Furthermore, sarcasm, emojis, and rapidly emerging new words restrict sentiment analysis from text data.…”

Section: Introductionmentioning

confidence: 99%

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

Lim

Ahn

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.

show abstract

Uses of facial expressions of android head system according to gender and age

Cited by 11 publications

References 13 publications

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

Building an automated engagement recognizer based on video analysis

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

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