masonzms.wordpress.com
十一月 | 2013 | Masonzms's Blog
https://masonzms.wordpress.com/2013/11
Archive for 十一月 2013. Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing. Mixed Membership Stochastic Blockmodels. JMLR (9) 2008 pp. 1981-2014. Loulwah AlSumait, Daniel Barbará, James Gentle, Carlotta Domeniconi. Topic Significance Ranking of LDA Generative Models. David Andrzejewski, Anne Mulhern, Ben Liblit, Xiaojin Zhu. Statistical Debugging using Latent Topic Models. ECML (2007). Arthur Asuncion, Padhraic Smyth, Max Welling. Asynchronous Distributed Learning of Topic Models. David M...
github.com
GitHub - cpsievert/LDAvis
https://github.com/cpsievert/LDAvis
No description or website provided. Use Git or checkout with SVN using the web URL. Jul 25, 2016. Polish language available as an additional language in serVis. Failed to load latest commit information. One global dictionary with column names correspoding to language. Jul 25, 2016. Jan 22, 2015. Merge branch 'master' of. Jul 25, 2016. Polish language is available to use in serVis. Jul 25, 2016. Bump version; update news; change location of a couple comments. Jul 17, 2015. CRAN version 0.3.2. Oct 24, 2015.
laceproject.eu
LACE Tech Focus -
http://www.laceproject.eu/tech
Playful Reading of Learning Analytics Papers using ‘off the shelf’ R packages. R is both a language and environment used by statisticians that I have been tinkering with recently for text analysis. I am no expert in statistics by any means, but I still like playing with the software because it has a large collection of add-on packages. I’ve taken some of the popular packages and techniques currently doing the rounds, modified them slightly and applied them to the the LAK dataset. 1) We import the rrdflib...
ryanmcd.com
GSLT: Machine Learning: Generalized Linear Classifiers
http://www.ryanmcd.com/courses/gslt2007/gslt2007.html
Generalized Linear Classifiers in NLP. For the better part of a decade machine learning methods like maximum entropy and support vector machines have been a major part of many NLP applications such as parsing, semantic role labeling, ontology induction, machine translation, and summarization. Many of these models fall into the class of Generalized Linear Classifiers. Date: October 22nd, 2007; Lecturer: Ryan McDonald. Outline (subject to change). Latest slides, will change]. Starter code available here.
rdinnager.github.io
Fitting topic models in R
http://rdinnager.github.io/notes/2014/07/20/mallet
Fitting topic models in R. I just came across a potentially useful R package called mallet. Which interfaces with MALLET. Blog comments powered by Disqus. 2015 Russell Dinnage with help from Jekyll Bootstrap.
hltcoe.jhu.edu
Archive
http://hltcoe.jhu.edu/events/archive/2010
Data Sets and Resources. Only show seminars with video. August 5, 2010. Ldquo;How Good is This Report, and Can a Machine Answer That Question? July 26, 2010. Ldquo;Unsupervised Methods for WSD”. The approaches I describe are widely applicable, and offer benefits to many NLP tasks, including information extraction, summarization, and machine translation. July 20, 2010. An informal look at the Meedan project [ http:/ meedan.net. Ed Bice is the founder of the Meedan project. He is a philosopher and form...
beyondbowties.academic.wlu.edu
Visualizations – Beyond Bow Ties
http://beyondbowties.academic.wlu.edu/category/media-type/visualizations
Washington and Lee University's Co-Education Decision. Map of Alumni Responses. May 14, 2014. From the MALLET homepage. MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. Lee university future women study hope raised position member successful idea respond expect fewer individuals top obtaining greatly conviction. This model concerns how the educational en...
blog.quantifyingkissinger.com
July | 2014 | "Everything on Paper Will Be Used Against Me:" Quantifying Kissinger
http://blog.quantifyingkissinger.com/2014/07
Everything on Paper Will Be Used Against Me: Quantifying Kissinger. Monthly Archives: July 2014. Area and Stream Graphs. Topic Modeling Stream Graphs. July 6, 2014. Interactive Topic Model Stream Graphs. Area and Stream Graphs. Topic Modeling Area Graphs. July 6, 2014. Memcons: Interactive Topic Model Area Graphs. Telcons: Interactive Topic Model Area Graphs. Line and Bar Graphs. Topic Model Stacked Bar Graphs. July 6, 2014. Memcons – Stacked Bar Graph. Telcons – Stacked Bar Graph. July 6, 2014. Memcons:...
blog.quantifyingkissinger.com
Sources and Process | "Everything on Paper Will Be Used Against Me:" Quantifying Kissinger
http://blog.quantifyingkissinger.com/category/methods/sources-and-process
Everything on Paper Will Be Used Against Me: Quantifying Kissinger. Category Archives: Sources and Process. About the Sources and Process. July 1, 2014. Kissinger Collection comprises 15,502 telephone conversation transcripts (telcons) and 2163 meeting memoranda transcripts (memcons). The resulting text files (spell checked but not corrected) were then processed using a number of tools. For Word Frequency and Collocation we used AntConc. For Topic Modeling we used MALLET. Word Frequency and Collocation.