Machine Learning

Digital Democracy Project: Making Government More Transparent one Video at a Time

Authorship: 
Sam Blakeslee, Alex Dekhtyar, Foaad Khosmood, Franz Kurfess, Toshihiro Kuboi, Hans Poschman, Giovanni Prinzivalli, Christine Robertson, Skylar Durst
Publication: 
Digital Humanities 2015
Poster
Location: 
Sydney, Australia
July, 2015
The Digital Democracy platform obtains data about the legislative committee hearings: the video archives, the information about the state legislature and so on. Figure 1 shows the design of the DD system. The main source of information for the DD platform is the Cal Channel video archive of legislative sessions, a service provided courtesy of cable TV companies that operate in California.

Combining Corpus-Based Features for Selecting Best Natural Language Sentences

Authorship: 
Foaad Khosmood and Robert Levinson
Publication: 
International Conference on Machine Learning and Applications
Conference
Location: 
Honolulu, Hawaii
December, 2011
Automated paraphrasing of natural language text has many interesting applications from aiding in better translations to generating better and more appropriate style language. In this paper, we are concerned with the problem of picking the best English sentence out of a set of machine generated paraphrase sentences, each designed to express the same content as a human generated original. We present a system of scoring sentences based on examples in large corpora.

Taxonomy and Evaluation of Markers for Computational Stylistics

Authorship: 
Foaad Khosmood and Robert Levinson
Publication: 
International Conference on Artificial Intelligence (ICAI)
Conference
Location: 
Las Vegas, Nevada
July, 2011
Currently, stylistic analysis of natural language texts is achieved through a wide variety of techniques containing many different algorithms, feature sets and collection methods. Most machine-learning methods rely on feature extraction to model the text and perform classification. But what are the best features for making style based distinctions? While many researchers have developed particular collections of style features – called style markers – no definitive list exists.
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