M
Morteza Karimzadeh
Researcher at University of Colorado Boulder
Publications - 31
Citations - 395
Morteza Karimzadeh is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Visual analytics & Geoparsing. The author has an hindex of 8, co-authored 28 publications receiving 248 citations. Previous affiliations of Morteza Karimzadeh include King Abdullah University of Science and Technology & Purdue University.
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Journal ArticleDOI
GeoCorpora: building a corpus to test and train microblog geoparsers
TL;DR: The GeoCorpora corpus building framework and software tools as well as a geo-annotated Twitter corpus built with these tools are presented to foster research and development in the areas of microblog/Twitter geoparsing and geographic information retrieval.
Journal ArticleDOI
GeoTxt: A scalable geoparsing system for unstructured text geolocation
TL;DR: GeoTxt offers six named entity recognition algorithms for place name recognition, and utilizes an enterprise search engine for the indexing, ranking, and retrieval of toponyms, enabling scalable geoparsing for streaming text.
Proceedings ArticleDOI
GeoTxt: a web API to leverage place references in text
Morteza Karimzadeh,Wenyi Huang,Siddhartha Banerjee,Jan Oliver Wallgrün,Frank Hardisty,Scott Pezanowski,Prasenjit Mitra,Alan M. MacEachren +7 more
TL;DR: GeoTxt is introduced, a web API plus human-usable web tool designed and implemented to tackle three components of place-reference processing from text, namely: extraction, disambiguation, and geolocation of place names mentioned in unstructured text.
Journal ArticleDOI
Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness
TL;DR: A novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements.
Journal ArticleDOI
VASSL: A Visual Analytics Toolkit for Social Spambot Labeling
TL;DR: VASSL, a visual analytics system that assists in the process of detecting and labeling spambots, enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling, enabling insights for the identification of spambot.