2 research outputs found
My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections
Comparative text mining extends from genre analysis and political bias
detection to the revelation of cultural and geographic differences, through to
the search for prior art across patents and scientific papers. These
applications use cross-collection topic modeling for the exploration,
clustering, and comparison of large sets of documents, such as digital
libraries. However, topic modeling on documents from different collections is
challenging because of domain-specific vocabulary. We present a
cross-collection topic model combined with automatic domain term extraction and
phrase segmentation. This model distinguishes collection-specific and
collection-independent words based on information entropy and reveals
commonalities and differences of multiple text collections. We evaluate our
model on patents, scientific papers, newspaper articles, forum posts, and
Wikipedia articles. In comparison to state-of-the-art cross-collection topic
modeling, our model achieves up to 13% higher topic coherence, up to 4% lower
perplexity, and up to 31% higher document classification accuracy. More
importantly, our approach is the first topic model that ensures disjunct
general and specific word distributions, resulting in clear-cut topic
representations
Browse-to-search
This demonstration presents a novel interactive online shopping application based on visual search technologies. When users want to buy something on a shopping site, they usually have the requirement of looking for related information from other web sites. Therefore users need to switch between the web page being browsed and other websites that provide search results. The proposed application enables users to naturally search products of interest when they browse a web page, and make their even causal purchase intent easily satisfied. The interactive shopping experience is characterized by: 1) in session - it allows users to specify the purchase intent in the browsing session, instead of leaving the current page and navigating to other websites; 2) in context - -the browsed web page provides implicit context information which helps infer user purchase preferences; 3) in focus - users easily specify their search interest using gesture on touch devices and do not need to formulate queries in search box; 4) natural-gesture inputs and visual-based search provides users a natural shopping experience. The system is evaluated against a data set consisting of several millions commercial product images. © 2012 Authors