Neural Sparse Topical Coding

Peng, Min; Xie, Qianqian; Zhang, Yanchun; Wang, Hua; Zhang, Xiuzhen; Huang, Jian; Tian, Gang

doi:10.18653/v1/p18-1217

Cited by 29 publications

(22 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The term data mining can be traced back to the 1950s when once large databases were searched using machine learning techniques. Over time, techniques evolved to be associated with data mining let become an integral part of modern database technologies and play an important role in the transition from classical data management Software tools that can directly support management decisions [32].…”

Section: Spatial-temporal Big Data Miningmentioning

confidence: 99%

The impact of Planned Special Events (PSEs) on urban traffic congestion

Fernando¹

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

The transport infrastructure of many cities has not been able to keep up with the pace of growth in the motorization rate or to counteract the intensification of urban traffic. Rising in traffic congestion in cities not only impacts the productivity costing billions but also responsible for more than 40% of all CO2 emissions which results in global warming. While expansion and construction of new roads may be considered in some cases, in most, better management of existing infrastructure to lower traffic congestion is the only option.The current state of the art commercial solutions can predict the recurring traffic situations, as this behavior can be easily learned from historical data. The challenge is to predict the non-recurrent congestion caused by events such as accidents, adverse weather, construction zones and Planned Special Event (PSE). Past research has shown, PSEs such as concerts or sports games, festivals and conventions has a huge impact on everyday urban transportation. Therefore, the aim of the proposed research is to investigate the impact PSEs on urban traffic congestion.The proposed research is applying spatial-temporal big data mining methods to predict the impact of PSEs on the urban concession. Specifically, the proposed research will consider the characteristics of PSEs such as location, type, duration, audience and time and day of the event in the proposed analysis approach which enables to predict of urban congestion for future PSEs.

show abstract

Section: Spatial-temporal Big Data Miningmentioning

confidence: 99%

The impact of Planned Special Events (PSEs) on urban traffic congestion

Fernando¹

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

show abstract

“…Feature selection–based software repositories mining: In terms of information extraction, most researchers mainly focus on textual content analysis such as distant supervision methods, topical coding, pattern clustering, and other topic models . However, in SE, most related work used the social features of StackOverflow to mine software repositories for specific software tasks such as code summarization and code search.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised software repositories mining and its application to code search

Peng

Yihan

et al. 2019

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

Software repositories are crucial resources for many software tasks, including code retrieval and annotation. Programming forums provide questions and answers (Q&A) from software developers, containing abundant code-description posts for exchanging knowledge about programming issues. However, most posts provide personal opinions of users that are often not adequately confirmed or outdated. Mining software repositories in such open and unrestricted forums is challenging. Since the posts can be arbitrary and noisy, it is difficult to get unified labels for supervised noise elimination. Different from existing mining approaches, this paper proposes Code-Description Mining Framework (CodeMF), an unsupervised framework to eliminate noisy posts and extract high quality software repositories from programming forums.CodeMF treats all social features of the posts as discrete-time signals for kernel principal component analysis and further performs wavelet transform feature fusion to find the delicate changes (noises in temporal signals). We conduct comprehensive experiments on StackOverflow. Experimental results demonstrate that CodeMF can effectively reduce running time and improve precision via mining high-quality software repositories for various programming languages, especially for the large-scale codebases. To further illustrate the effect of CodeMF applied in software tasks, we introduce it to improve the performance of query-expansion code search. Meanwhile, for SQL and C# programs, compared to the state-of-the-art query-expansion method QECK, the improvement of QECK CodeMF is 2% and 6% on Recall@10, and 4% and 14% on mean reciprocal rank, respectively.

show abstract

“…They didn't reassumed inference algorithms. [17] Qianqian Xie et al suggested a model named Bayesian Sparse Topical Coding with Poisson Distribution which differs from conventional STMs. While the STMs are the Sparse Topic Models used for understanding and learning semantic meaning of short texts.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams

Sahar¹,

Irshad

Khan

2019

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

Today the computational study of people's opinion expressed in free form written text is called the field of sentiment analysis and opinion mining. Various research areas such as Natural Language Processing, Data Mining, Text Mining lie in field of Sentiment Analysis and is also becoming major part of importance to organizations because of online commerce is included in their operational strategy. Due to excess of user's comments, feedback on web there is a need to analyze the user generated text. This research focuses on aspect level sentiment analysis in which identification of aspects and their related sentiments is being done. Opinion analysis helps to identify the polarity of the text and feature extraction. This study is being done to provide an effective and efficient framework to calculate the sentiments of written text by using Naïve Bayes approach. For sentiment analysis dataset of 1060 reviews of different restaurants from online website TripAdvisor.com is being used. The outcome achieved good accuracy 80.833 percent.

show abstract

Neural Sparse Topical Coding

Cited by 29 publications

References 9 publications

The impact of Planned Special Events (PSEs) on urban traffic congestion

The impact of Planned Special Events (PSEs) on urban traffic congestion

Unsupervised software repositories mining and its application to code search

Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams

Contact Info

Product

Resources

About