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Knowledge and Data Engineering

SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming


Winter 2017 Seasonal School Workshop, Malaysia. The Arduino Simple Programming at school was held at five different schools near Parit Raja, Batu Pahat, Johor from October-November 2017. The schools are SK Jelutong, SMK Sri gading, SK Pintas Puding, SK Bukit Kuari and SK Seri Sabak Uni. The objective of Professional Knowledge Transfer Workshop is to give an exposure to the students of primary and secondary schools on engineering and to inspire them to be engineers. This program was organized by the Institute of Electrical and Electronics Engineers (IEEE) UTHM Student Branch with cooperation of IEEE Signal Processing Society (SPS) Malaysia Chapter. The program is fully funded by IEEE Signal Processing Society (SPS) under the IEEE SPS Member-Driven Initiative Program with the amount of USD 1,400. The fund had been used to buy 30 Arduino Uno kits, purchasing stationery, and refreshments for 30 students from each school, 10 postgraduate students from UTHM IEEE student branch and 13 Academic staff from FKEE, UTHM.

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Authors:
Dr Mohd Norzali Hj Mohd
Submitted On:
13 December 2017 - 11:05am
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Fasa3_latestEdited module.pdf

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[1] Dr Mohd Norzali Hj Mohd, "SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2372. Accessed: Dec. 16, 2017.
@article{2372-17,
url = {http://sigport.org/2372},
author = {Dr Mohd Norzali Hj Mohd },
publisher = {IEEE SigPort},
title = {SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming},
year = {2017} }
TY - EJOUR
T1 - SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming
AU - Dr Mohd Norzali Hj Mohd
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2372
ER -
Dr Mohd Norzali Hj Mohd. (2017). SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming. IEEE SigPort. http://sigport.org/2372
Dr Mohd Norzali Hj Mohd, 2017. SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming. Available at: http://sigport.org/2372.
Dr Mohd Norzali Hj Mohd. (2017). "SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming." Web.
1. Dr Mohd Norzali Hj Mohd. SPS Malaysia Chapter Member-Driven Initiative: Professional Knowledge Transfer Workshop 2017: Arduino Simple Programming [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2372

Automatic Question-answering Using a Deep Similarity Neural Network


Automatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. In this work, we propose a deep learning based model for automatic question-answering. First the questions and answers are embedded using neural probabilistic modeling. Then a deep similarity neural network is trained to find the similarity score of a pair of answer and question. Then for each question, the best answer is found as the one with the highest similarity score.

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Authors:
Shervin Minaee, Zhu Liu
Submitted On:
13 November 2017 - 9:10am
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GlobalSIP2017Poster-QAEngine.pdf

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[1] Shervin Minaee, Zhu Liu, "Automatic Question-answering Using a Deep Similarity Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2335. Accessed: Dec. 16, 2017.
@article{2335-17,
url = {http://sigport.org/2335},
author = {Shervin Minaee; Zhu Liu },
publisher = {IEEE SigPort},
title = {Automatic Question-answering Using a Deep Similarity Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automatic Question-answering Using a Deep Similarity Neural Network
AU - Shervin Minaee; Zhu Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2335
ER -
Shervin Minaee, Zhu Liu. (2017). Automatic Question-answering Using a Deep Similarity Neural Network. IEEE SigPort. http://sigport.org/2335
Shervin Minaee, Zhu Liu, 2017. Automatic Question-answering Using a Deep Similarity Neural Network. Available at: http://sigport.org/2335.
Shervin Minaee, Zhu Liu. (2017). "Automatic Question-answering Using a Deep Similarity Neural Network." Web.
1. Shervin Minaee, Zhu Liu. Automatic Question-answering Using a Deep Similarity Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2335

Convolutional Factor Analysis Inspired Compressvie Sensing


We solve the compressive sensing problem via convolutional factor analysis, where the convolutional dictionaries are learned in situ from the compressed measurements. An alternating direction method of multipliers (ADMM) paradigm for compressive sensing inversion based on convolutional factor analysis is developed. The proposed algorithm provides reconstructed images as well as features, which can be directly used for recognition (e:g:, classification) tasks.

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Authors:
Yunchen Pu
Submitted On:
17 September 2017 - 11:10am
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Slides_ICIP_2017.pdf

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[1] Yunchen Pu, "Convolutional Factor Analysis Inspired Compressvie Sensing", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2216. Accessed: Dec. 16, 2017.
@article{2216-17,
url = {http://sigport.org/2216},
author = {Yunchen Pu },
publisher = {IEEE SigPort},
title = {Convolutional Factor Analysis Inspired Compressvie Sensing},
year = {2017} }
TY - EJOUR
T1 - Convolutional Factor Analysis Inspired Compressvie Sensing
AU - Yunchen Pu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2216
ER -
Yunchen Pu. (2017). Convolutional Factor Analysis Inspired Compressvie Sensing. IEEE SigPort. http://sigport.org/2216
Yunchen Pu, 2017. Convolutional Factor Analysis Inspired Compressvie Sensing. Available at: http://sigport.org/2216.
Yunchen Pu. (2017). "Convolutional Factor Analysis Inspired Compressvie Sensing." Web.
1. Yunchen Pu. Convolutional Factor Analysis Inspired Compressvie Sensing [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2216

AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering


One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC.

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Authors:
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero
Submitted On:
5 March 2017 - 11:06pm
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ICASSP_AMOS_2017.pdf

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[1] Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1643. Accessed: Dec. 16, 2017.
@article{1643-17,
url = {http://sigport.org/1643},
author = {Pin-Yu Chen; Thibaut Gensollen; Alfred Hero },
publisher = {IEEE SigPort},
title = {AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering},
year = {2017} }
TY - EJOUR
T1 - AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
AU - Pin-Yu Chen; Thibaut Gensollen; Alfred Hero
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1643
ER -
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. IEEE SigPort. http://sigport.org/1643
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, 2017. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. Available at: http://sigport.org/1643.
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering." Web.
1. Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1643

Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation


Keyphrases are short phrases that best represent a document content. They can be useful in a variety of applications, including document summarization and retrieval models. In this paper, we introduce the first dataset of keyphrases for an Arabic document collection, obtained by means of crowdsourcing. We experimentally evaluate different crowdsourced answer aggregation strategies and validate their performances against expert annotations to evaluate the quality of our dataset. We report about our experimental results, the dataset features, some lessons learned, and ideas for future

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Authors:
Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini
Submitted On:
30 November 2016 - 4:11am
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Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation

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[1] Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini, "Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1321. Accessed: Dec. 16, 2017.
@article{1321-16,
url = {http://sigport.org/1321},
author = {Marco Basaldella; Eddy Maddalena; Stefano Mizzaro; Gianluca Demartini },
publisher = {IEEE SigPort},
title = {Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation},
year = {2016} }
TY - EJOUR
T1 - Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation
AU - Marco Basaldella; Eddy Maddalena; Stefano Mizzaro; Gianluca Demartini
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1321
ER -
Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini. (2016). Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation. IEEE SigPort. http://sigport.org/1321
Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini, 2016. Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation. Available at: http://sigport.org/1321.
Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini. (2016). "Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation." Web.
1. Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini. Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1321

Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction


Arabic is one of the fastest growing languages on the Web, with an increasing amount of user generated content being published by both native and non-native speakers all over the world. Despite the great linguistic differences between Arabic and western languages such as English, most Arabic keyphrase extraction systems rely on approaches designed for western languages, thus ignoring its rich morphology and syntax. In this paper we present a new approach leveraging the Arabic morphology and syntax to generate a restricted set of meaningful candidates among which keyphrases are selected.

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Authors:
Dario De Nart, Dante Degl’Innocenti, Carlo Tasso
Submitted On:
30 November 2016 - 4:12am
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Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction

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[1] Dario De Nart, Dante Degl’Innocenti, Carlo Tasso, "Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1320. Accessed: Dec. 16, 2017.
@article{1320-16,
url = {http://sigport.org/1320},
author = {Dario De Nart; Dante Degl’Innocenti; Carlo Tasso },
publisher = {IEEE SigPort},
title = {Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction},
year = {2016} }
TY - EJOUR
T1 - Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction
AU - Dario De Nart; Dante Degl’Innocenti; Carlo Tasso
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1320
ER -
Dario De Nart, Dante Degl’Innocenti, Carlo Tasso. (2016). Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction. IEEE SigPort. http://sigport.org/1320
Dario De Nart, Dante Degl’Innocenti, Carlo Tasso, 2016. Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction. Available at: http://sigport.org/1320.
Dario De Nart, Dante Degl’Innocenti, Carlo Tasso. (2016). "Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction." Web.
1. Dario De Nart, Dante Degl’Innocenti, Carlo Tasso. Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1320

Annotating Chinese Noun Phrases Based on Semantic Dependency Graph


Annotating complicated noun phrases is a difficulty in semantic analysis. In this paper we investigate the annotation methods of noun phrases in Nombank, Chinese Nombank and Sinica Treebank trying to propose an annotation scheme based on semantic dependency graph for noun phrases.

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Authors:
Shao Yanqiu
Submitted On:
29 November 2016 - 4:06am
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Annotating Chinese Noun Phrases Based on Semantic Dependency Graph_IALP.pdf

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[1] Shao Yanqiu, "Annotating Chinese Noun Phrases Based on Semantic Dependency Graph", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1318. Accessed: Dec. 16, 2017.
@article{1318-16,
url = {http://sigport.org/1318},
author = {Shao Yanqiu },
publisher = {IEEE SigPort},
title = {Annotating Chinese Noun Phrases Based on Semantic Dependency Graph},
year = {2016} }
TY - EJOUR
T1 - Annotating Chinese Noun Phrases Based on Semantic Dependency Graph
AU - Shao Yanqiu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1318
ER -
Shao Yanqiu. (2016). Annotating Chinese Noun Phrases Based on Semantic Dependency Graph. IEEE SigPort. http://sigport.org/1318
Shao Yanqiu, 2016. Annotating Chinese Noun Phrases Based on Semantic Dependency Graph. Available at: http://sigport.org/1318.
Shao Yanqiu. (2016). "Annotating Chinese Noun Phrases Based on Semantic Dependency Graph." Web.
1. Shao Yanqiu. Annotating Chinese Noun Phrases Based on Semantic Dependency Graph [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1318

Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models


This work focuses on two specific types of sentimental information analysis for traditional Chinese words, i.e., valence represents the degree of pleasant and unpleasant feelings (i.e., sentiment orientation), and arousal represents the degree of excitement and calm (i.e., sentiment strength). To address it, we proposed supervised ensemble learning models to assign appropriate real valued ratings to each

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Authors:
Feixiang Wang, Yunxiao Zhou, Lan man
Submitted On:
27 November 2016 - 11:06pm
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IALP-117-slides

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[1] Feixiang Wang, Yunxiao Zhou, Lan man, "Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1314. Accessed: Dec. 16, 2017.
@article{1314-16,
url = {http://sigport.org/1314},
author = {Feixiang Wang; Yunxiao Zhou; Lan man },
publisher = {IEEE SigPort},
title = {Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models},
year = {2016} }
TY - EJOUR
T1 - Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models
AU - Feixiang Wang; Yunxiao Zhou; Lan man
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1314
ER -
Feixiang Wang, Yunxiao Zhou, Lan man. (2016). Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models. IEEE SigPort. http://sigport.org/1314
Feixiang Wang, Yunxiao Zhou, Lan man, 2016. Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models. Available at: http://sigport.org/1314.
Feixiang Wang, Yunxiao Zhou, Lan man. (2016). "Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models." Web.
1. Feixiang Wang, Yunxiao Zhou, Lan man. Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1314

Importance Weighted Feature Selection Strategy for Text Classification


Feature selection, which aims at obtaining a compact and effective feature subset for better performance and higher efficiency, has been studied for decades. The traditional feature selection metrics, such as Chi-square and information gain, fail to consider how important a feature is in a document. Features, no matter how much effective semantic information they hold, are treated equally. Intuitively, thus calculated feature selection metrics are very likely to introduce much noise. We, therefore, in this study, extend the work of Li et al.

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Authors:
Baoli Li
Submitted On:
27 November 2016 - 10:44am
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IALP2016-113-baoli-v0.2.pdf

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[1] Baoli Li, "Importance Weighted Feature Selection Strategy for Text Classification", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1312. Accessed: Dec. 16, 2017.
@article{1312-16,
url = {http://sigport.org/1312},
author = {Baoli Li },
publisher = {IEEE SigPort},
title = {Importance Weighted Feature Selection Strategy for Text Classification},
year = {2016} }
TY - EJOUR
T1 - Importance Weighted Feature Selection Strategy for Text Classification
AU - Baoli Li
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1312
ER -
Baoli Li. (2016). Importance Weighted Feature Selection Strategy for Text Classification. IEEE SigPort. http://sigport.org/1312
Baoli Li, 2016. Importance Weighted Feature Selection Strategy for Text Classification. Available at: http://sigport.org/1312.
Baoli Li. (2016). "Importance Weighted Feature Selection Strategy for Text Classification." Web.
1. Baoli Li. Importance Weighted Feature Selection Strategy for Text Classification [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1312

Learning Dimensional Sentiment of Traditional Chinese Words with Word Embedding and Support Vector Regression


Dimensional sentiment analysis approach, which represents affective states as continuous numerical values on multiple dimensions, such as valence-arousal (VA) space, allows for more fine-grained analysis than the traditional categorical approach. In recent years, it has been applied in applications such as antisocial behavior detection, mood analysis and product review ranking. In this approach, an affective lexicon with dimensional sentiment values is a key resource, but building such a lexicon costs much.

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Submitted On:
27 November 2016 - 10:39am
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IALP2016_124_Baoli_poster1.pdf

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[1] , "Learning Dimensional Sentiment of Traditional Chinese Words with Word Embedding and Support Vector Regression ", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1311. Accessed: Dec. 16, 2017.
@article{1311-16,
url = {http://sigport.org/1311},
author = { },
publisher = {IEEE SigPort},
title = {Learning Dimensional Sentiment of Traditional Chinese Words with Word Embedding and Support Vector Regression },
year = {2016} }
TY - EJOUR
T1 - Learning Dimensional Sentiment of Traditional Chinese Words with Word Embedding and Support Vector Regression
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1311
ER -
. (2016). Learning Dimensional Sentiment of Traditional Chinese Words with Word Embedding and Support Vector Regression . IEEE SigPort. http://sigport.org/1311
, 2016. Learning Dimensional Sentiment of Traditional Chinese Words with Word Embedding and Support Vector Regression . Available at: http://sigport.org/1311.
. (2016). "Learning Dimensional Sentiment of Traditional Chinese Words with Word Embedding and Support Vector Regression ." Web.
1. . Learning Dimensional Sentiment of Traditional Chinese Words with Word Embedding and Support Vector Regression [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1311

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