Monday I presented the work we have been doing for over 1 year. We are using dynamic topic modeling and cross-domain correlations to understand how climate change research is influencing the Intergovernmental Panel for Climate Change Assessments.
Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports
This work is just getting started, it took us a year to get the IPCC documents and citations parsed and processed, the climate change glossaries built, the preprocessing steps to get the best out of the topic modeling, and parameter tweaking. Now we are going to start large scale experimentation.
Thanks to the best advisors ever, Dr. Finin, Dr. Halem and thanks to Dr. Cane, who is a great scientist to work with. It is a honor to work with such great people.
Showing posts with label LDA. Show all posts
Showing posts with label LDA. Show all posts
Thursday, December 8, 2016
Friday, November 20, 2015
GenSim and LDA
This is a nice little simple tutorial on using GenSim to exercise LDA.
GenSim LDA Tutuorial
GenSim LDA Tutuorial
Friday, October 3, 2014
Taming Wild Big Data
Our latest paper for the AAAI Fall Symposium.
Abstract: Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tabular data that can be transformed into such a graph. We start by applying topic models to contextualize the data and then use the results to identify the potential types of the graph’s nodes by mapping them to known types found in large open ontologies such as Freebase, and DBpedia. The results allow us to assemble coarse clusters of objects that can then be used to interpret the link and perform entity disambiguation and record linking.
Abstract: Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tabular data that can be transformed into such a graph. We start by applying topic models to contextualize the data and then use the results to identify the potential types of the graph’s nodes by mapping them to known types found in large open ontologies such as Freebase, and DBpedia. The results allow us to assemble coarse clusters of objects that can then be used to interpret the link and perform entity disambiguation and record linking.
Tuesday, April 9, 2013
LDA - Step by Step in R
Was able to get an example running very quickly with this tutorial.
http://www.rtexttools.com/1/post/2011/08/getting-started-with-latent-dirichlet-allocation-using-rtexttools-topicmodels.html
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