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Knowledge and Data Engineering

Alternating autoencoders for matrix completion


We consider autoencoders (AEs) for matrix completion (MC) with application to collaborative filtering (CF) for recommedation systems. It is observed that for a given sparse user-item rating matrix, denoted asM, an AE performs matrix factorization so that the recovered matrix is represented as a product of user and item feature matrices.

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Authors:
Kiwon Lee, Yong H. Lee, Changho Suh
Submitted On:
4 June 2018 - 2:48pm
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Poster_Lee.pdf

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[1] Kiwon Lee, Yong H. Lee, Changho Suh, "Alternating autoencoders for matrix completion", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3223. Accessed: Aug. 21, 2018.
@article{3223-18,
url = {http://sigport.org/3223},
author = {Kiwon Lee; Yong H. Lee; Changho Suh },
publisher = {IEEE SigPort},
title = {Alternating autoencoders for matrix completion},
year = {2018} }
TY - EJOUR
T1 - Alternating autoencoders for matrix completion
AU - Kiwon Lee; Yong H. Lee; Changho Suh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3223
ER -
Kiwon Lee, Yong H. Lee, Changho Suh. (2018). Alternating autoencoders for matrix completion. IEEE SigPort. http://sigport.org/3223
Kiwon Lee, Yong H. Lee, Changho Suh, 2018. Alternating autoencoders for matrix completion. Available at: http://sigport.org/3223.
Kiwon Lee, Yong H. Lee, Changho Suh. (2018). "Alternating autoencoders for matrix completion." Web.
1. Kiwon Lee, Yong H. Lee, Changho Suh. Alternating autoencoders for matrix completion [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3223

Profit Maximizing Logistic Regression Modeling for Credit Scoring


Multiple classification techniques have been employed for different business applications. In the particular case of credit scoring, a classifier which maximizes the total profit is preferable. The recently proposed expected maximum profit (EMP) measure for credit scoring allows to select the most profitable classifier. Taking the idea of the EMP one step further, it is desirable to integrate the measure into model construction, and thus obtain a profit maximizing model.

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Authors:
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme
Submitted On:
30 May 2018 - 8:30pm
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PosterA1_Arnout_Devos_DSW2018.pdf

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[1] Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, "Profit Maximizing Logistic Regression Modeling for Credit Scoring", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3221. Accessed: Aug. 21, 2018.
@article{3221-18,
url = {http://sigport.org/3221},
author = {Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme },
publisher = {IEEE SigPort},
title = {Profit Maximizing Logistic Regression Modeling for Credit Scoring},
year = {2018} }
TY - EJOUR
T1 - Profit Maximizing Logistic Regression Modeling for Credit Scoring
AU - Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3221
ER -
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). Profit Maximizing Logistic Regression Modeling for Credit Scoring. IEEE SigPort. http://sigport.org/3221
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, 2018. Profit Maximizing Logistic Regression Modeling for Credit Scoring. Available at: http://sigport.org/3221.
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). "Profit Maximizing Logistic Regression Modeling for Credit Scoring." Web.
1. Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. Profit Maximizing Logistic Regression Modeling for Credit Scoring [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3221

PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS


In recent years, neural networks (NN) have achieved remarkable
performance improvement in text classification due to
their powerful ability to encode discriminative features by
incorporating label information into model training. Inspired
by the success of NN in text classification, we propose a
pseudo-supervised neural network approach for text clustering.
The neural network is trained in a supervised fashion
with pseudo-labels, which are provided by the cluster labels
of pre-clustering on unsupervised document representations.

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Authors:
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling
Submitted On:
13 April 2018 - 3:58am
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ICASSP-Chen Peixin_v2.pdf

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[1] Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling, "PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2640. Accessed: Aug. 21, 2018.
@article{2640-18,
url = {http://sigport.org/2640},
author = {Peixin Chen; Wu Guo; Lirong Dai; Zhenhua Ling },
publisher = {IEEE SigPort},
title = {PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS
AU - Peixin Chen; Wu Guo; Lirong Dai; Zhenhua Ling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2640
ER -
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. (2018). PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS. IEEE SigPort. http://sigport.org/2640
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling, 2018. PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS. Available at: http://sigport.org/2640.
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. (2018). "PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS." Web.
1. Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2640

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: Aug. 21, 2018.
@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: Aug. 21, 2018.
@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: Aug. 21, 2018.
@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: Aug. 21, 2018.
@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: Aug. 21, 2018.
@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.

Paper Details

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: Aug. 21, 2018.
@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: Aug. 21, 2018.
@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

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