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. "Time for dabs": Analyzing Twitter data on marijuana concentrates across the U.S. Drug and Alcohol Dependence. 2015 ;155:307-311.
. "Time for dabs": Analyzing Twitter data on marijuana concentrates across the U.S. Drug and Alcohol Dependence. 2015 ;155:307-311.
. Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples. 2016 . 
(994.44 KB)
(994.44 KB). Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples. In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany: ACM; 2017. p. 1-9. (701.11 KB)
(701.11 KB). Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples. In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany: ACM; 2017. p. 1-9. (701.11 KB)
(701.11 KB). Analyzing the Social Media Footprint of Street Gangs. In 2015 IEEE International Conference on Intelligence and Security Informatics (ISI). Baltimore, MD: IEEE; 2015. p. 91-96. (1.26 MB)
(1.26 MB). A Statistical and Schema Independent Approach to Identify Equivalent Properties on Linked Data. 9th International Conference on Semantic Systems (I-SEMANTICS). Graz, Austria; 2013. p. 33-40. 
(1.86 MB) (635.53 KB)
(1.86 MB) (635.53 KB). A Statistical and Schema Independent Approach to Identify Equivalent Properties on Linked Data. 9th International Conference on Semantic Systems (I-SEMANTICS). Graz, Austria; 2013. p. 33-40. (1.86 MB) (635.53 KB)
(1.86 MB) (635.53 KB). Semantic Annotation and Search for resources in the next Generation Web with SA-REST. In W3C Workshop on Data and Services Integration; 2011.
. A Semantics-Based Measure of Emoji Similarity. In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany: ACM; 2017. p. 646-653. (2.34 MB)
(2.34 MB). Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples. In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany: ACM; 2017. p. 1-9. (701.11 KB)
(701.11 KB). Feature Engineering for Twitter-based Applications. In Feature Engineering for Machine Learning and Data Analytics. New York: Chapman and Hall. Data Mining and Knowledge Discovery Series; 2018. p. 359-393. (684.81 KB)
(684.81 KB). A Framework to Understand Emoji Meaning: Similarity and Sense Disambiguation of Emoji using EmojiNet. College of Engineering and Computer Science. [Dayton]: Wright State University; 2018. p. 111.
. "Time for dabs": Analyzing Twitter data on marijuana concentrates across the U.S. Drug and Alcohol Dependence. 2015 ;155:307-311.
. Analyzing the Social Media Footprint of Street Gangs. In 2015 IEEE International Conference on Intelligence and Security Informatics (ISI). Baltimore, MD: IEEE; 2015. p. 91-96. (1.26 MB)
(1.26 MB). A Framework to Understand Emoji Meaning: Similarity and Sense Disambiguation of Emoji using EmojiNet. College of Engineering and Computer Science. [Dayton]: Wright State University; 2018. p. 111.
. Finding Street Gang Members on Twitter. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016). San Francisco, CA, USA; 2016. p. 685-692. (2.02 MB)
(2.02 MB). Word Embeddings to Enhance Twitter Gang Member Profile Identification. In IJCAI Workshop on Semantic Machine Learning (SML 2016). New York City, NY: CEUR-WS; 2016. p. 18-24. (265.83 KB) (6.23 MB)
(265.83 KB) (6.23 MB). A Statistical and Schema Independent Approach to Identify Equivalent Properties on Linked Data. 9th International Conference on Semantic Systems (I-SEMANTICS). Graz, Austria; 2013. p. 33-40. (1.86 MB) (635.53 KB)
(1.86 MB) (635.53 KB). Finding Street Gang Members on Twitter. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016). San Francisco, CA, USA; 2016. p. 685-692. (2.02 MB)
(2.02 MB). Word Embeddings to Enhance Twitter Gang Member Profile Identification. In IJCAI Workshop on Semantic Machine Learning (SML 2016). New York City, NY: CEUR-WS; 2016. p. 18-24. (265.83 KB) (6.23 MB)
(265.83 KB) (6.23 MB). Feature Engineering for Twitter-based Applications. In Feature Engineering for Machine Learning and Data Analytics. New York: Chapman and Hall. Data Mining and Knowledge Discovery Series; 2018. p. 359-393. (684.81 KB)
(684.81 KB). "Time for dabs": Analyzing Twitter data on marijuana concentrates across the U.S. Drug and Alcohol Dependence. 2015 ;155:307-311.
. Feature Engineering for Twitter-based Applications. In Feature Engineering for Machine Learning and Data Analytics. New York: Chapman and Hall. Data Mining and Knowledge Discovery Series; 2018. p. 359-393. (684.81 KB)
(684.81 KB). A Framework to Understand Emoji Meaning: Similarity and Sense Disambiguation of Emoji using EmojiNet. College of Engineering and Computer Science. [Dayton]: Wright State University; 2018. p. 111.
. "Time for dabs": Analyzing Twitter data on butane hash oil use. In College on Problems of Drug Dependence CPDD 2015. Phoenix, Arizona; 2015. (87.2 KB)
(87.2 KB). Feature Engineering for Twitter-based Applications. In Feature Engineering for Machine Learning and Data Analytics. New York: Chapman and Hall. Data Mining and Knowledge Discovery Series; 2018. p. 359-393. (684.81 KB)
(684.81 KB). A Keyword Sense Disambiguation Based Approach for Noise Filtering in Twitter. In 1st Insight Student Conference. Dublin, Ireland: University College Dublin; 2014. (85.5 KB)
(85.5 KB). Finding Street Gang Members on Twitter. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016). San Francisco, CA, USA; 2016. p. 685-692. (2.02 MB)
(2.02 MB). Word Embeddings to Enhance Twitter Gang Member Profile Identification. In IJCAI Workshop on Semantic Machine Learning (SML 2016). New York City, NY: CEUR-WS; 2016. p. 18-24. (265.83 KB) (6.23 MB)
(265.83 KB) (6.23 MB). Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples. 2016 . (994.44 KB)
(994.44 KB). Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples. In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany: ACM; 2017. p. 1-9. (701.11 KB)
(701.11 KB). A Framework to Understand Emoji Meaning: Similarity and Sense Disambiguation of Emoji using EmojiNet. College of Engineering and Computer Science. [Dayton]: Wright State University; 2018. p. 111.
. Word Embeddings to Enhance Twitter Gang Member Profile Identification. In IJCAI Workshop on Semantic Machine Learning (SML 2016). New York City, NY: CEUR-WS; 2016. p. 18-24. (265.83 KB) (6.23 MB)
(265.83 KB) (6.23 MB). A Keyword Sense Disambiguation Based Approach for Noise Filtering in Twitter. In 1st Insight Student Conference. Dublin, Ireland: University College Dublin; 2014. (85.5 KB)
(85.5 KB)