Combining the Missing Link: An Incremental Topic Model of Document Content and Hyperlink Authors: Huifang Ma, Zhixin Li and Zhongzhi Shi Keywords: Topic model, Link-IPLSI, Incremental Learning, Adaptive Asymmetric learning
The content and structure of linked information such as sets of web pages or research paper archives are dynamic and keep on changing. Even though different methods are proposed to exploit both the link structure and the content information, no existing approach can effectively deal with this evolution. We propose a novel joint model, called Link-IPLSI, to combine texts and links in a topic modeling framework incrementally. The model takes advantage of a novel link updating technique that can cope with dynamic changes of online document streams in a faster and scalable way. Furthermore, an adaptive asymmetric learning method is adopted to freely control the assignment of weights to terms and citations. Experimental results on two different sources of online information demonstrate the time saving strength of our method and indicate that our model leads to systematic improvements in the quality of classification.
- Timed expiration of documents and terms? Appropriate for library settings? (discussion)
- Citations treated same as hyperlinks? (Aren’t citations more granular?) (3-5 pages, citations)
- What do we lose by citation to documents and not concepts/locations in documents? (3-5 pages, citations)
PS: The updating aspects of this paper are very important. Static data exists but isn’t very common in enterprise applications.