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Jun-Ping Ng
Bloomberg
New York, New York
 

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.


     

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. They are, how...

 

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 transformations of ...

 

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 topics associated...

 

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 words not based ...

 

Sequential summarization: a full view of Twitter trending topics
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....

 
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