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Ng, JunPing
Bloomberg
New York, New York
 
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Jun Ping is an avid researcher and engineer. He is currently a software engineer in the machine learning team at Bloomberg. There, he is involved in building up and improving many of the natural language processing (NLP) pipelines and systems, focusing on problems including sentiment analysis and topic detection.

Prior to joining Bloomberg, Jun Ping had been active in the entrepreneurship scene in his home country, Singapore. He founded several start-ups, including Jayeson Solutions, which delivers technologies and platforms to assist clients with large-scale algorithmic trading.

Parallel to his work in the industry, Jun Ping is also a keen educator. He has taught classes on information security and operating systems, both as an adjunct lecturer at Nanyang Polytechnic, and as a teaching assistant at the National University of Singapore.

Jun Ping graduated with a PhD in Computer Science from the National University of Singapore. His research thesis looked at the extraction of temporal information from free text, and how it can be applied to improve multi-document news summarization. Besides reviewing for Computing Reviews, Jun Ping had previously been invited to review for several leading conferences including COLING and IJCNLP.

 
 
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- 10 of 10 reviews

   
  Have you lost the thread? Discovering ongoing conversations in scattered dialog blocks
Zanzotto F., Ferrone L. ACM Transactions on Interactive Intelligent Systems 7(2): 1-19, 2017.  Type: Article

This interesting read addresses the problem of discovering conversations within dialog blocks. Parallel conversational threads occur in many scenarios, such as within an online forum, in an instant message session, and in emails. Being...

Dec 4 2017  
  Recommender systems handbook
Ricci F., Rokach L., Shapira B., Springer Publishing Company, Incorporated, New York, NY, 2015. 1003 pp.  Type: Book (978-1-489976-36-9)

If you have time for just one book to get yourself up to speed with the latest and best in recommender systems, this is the book you want. Recommender systems handbook is a carefully edited book that covers a wide range of topic...

Apr 7 2016  
  A novel hybrid approach improving effectiveness of recommender systems
Sarnè G. Journal of Intelligent Information Systems 44(3): 397-414, 2015.  Type: Article

Recommendation systems are an important part of many online services that we have grown accustomed to. They automatically suggest movies you may like to watch on Netflix, or products that you may otherwise not have noticed on Amazon. T...

Sep 30 2015  
  Novel harmony search-based algorithms for part-of-speech tagging
Forsati R., Shamsfard M. Knowledge and Information Systems 42(3): 709-736, 2015.  Type: Article

The application of the harmony search algorithm to the well-known problem of part-of-speech (POS) tagging is described in this paper. The harmony search algorithm involves starting from an initial state, and through a series of transfo...

Jun 1 2015  
   A platform for language independent summarization
Cabral L., Lins R., Mello R., Freitas F., Ávila B., Simske S., Riss M.  DocEng 2014 (Proceedings of the 2014 ACM Symposium on Document Engineering, Fort Collins, CO, Sep 16-19, 2014) 203-206, 2014.  Type: Proceedings

This paper describes a language independent, automatic text summarization system, the platform for language independent summarization (PLIS). Traditional text summarization systems mainly focus on the processing of English text. PLIS i...

Nov 4 2014  
   Do ordinary bloggers really differ from blog celebrities?
Koltsova O., Koltcov S., Alexeeva S.  WebSci 2014 (Proceedings of the 2014 ACM Conference on Web Science, Bloomington, IN, Jun 23-26, 2014) 166-170, 2014.  Type: Proceedings

Are there differences between popular and regular bloggers? This paper seeks to answer this question and describe some potential differences. It examines three hypotheses: (H1) there is a difference in the main topics discussed by thes...

Sep 23 2014  
  Sequential summarization: a full view of Twitter trending topics
Mladenovic M., Gao D., Li W., Cai X., Zhang R., Ouyang Y. IEEE/ACM Transactions on Audio, Speech and Language Processing 22(2): 293-302, 2014.  Type: Article

This paper describes an approach to summarize trending topics on Twitter. The tweets for a given topic are linearly segmented into more granular subtopics that define a single action or event....

Jul 11 2014  
  Normalization of informal text
Pennell D., Liu Y. Computer Speech and Language 28(1): 256-277, 2014.  Type: Article

This detailed and well-written paper presents a study on the normalization of informal text. The idea of normalization is to convert or correct informal language use into its formal equivalent. An example would be the expansion of the ...

May 9 2014  
  Improving text classification accuracy by training label cleaning
Esuli A., Sebastiani F. ACM Transactions on Information Systems 31(4): 1-28, 2013.  Type: Article

A large-scale study on the use of training label cleaning (TLC) to improve text classification is described in this paper. The purpose of TLC is to identify potentially mislabeled instances in a training dataset, and to flag them for c...

Feb 12 2014  
   Syntactic N-grams as machine learning features for natural language processing
Sidorov G., Velasquez F., Stamatatos E., Gelbukh A., Chanona-Hernández L. Expert Systems with Applications: An International Journal 41(3): 853-860, 2014.  Type: Article

Traditionally, n-grams are derived by extracting groups of words as they appear in a text. This paper describes a new way to formulate n-grams, referred to as syntactic n-grams (sn-grams). Sn-grams are made by extracting groups of word...

Jan 6 2014  
 
 
 
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