Rohini Srihari is an educator, computer scientist and enterpreneur.
Her work has spanned the areas of artificial intelligence, multimedia information retrieval, computational linguistics and text mining.
She is an author of over 150 papers in computer science, including two US patents, which are well-cited (h-index > 30)
and supervised ten doctoral dissertations.
Most recently she has been writing technology articles for the general public, e.g., internet of everything, personalized marketing, etc.
She founded several natural language technology companies, including:
(i) Cymfony (CEO 1996-2001, Chief Scientist 2001-2005). Cymfony became the leading market analytics company and was acquired by TNS.
(ii) Janya (CEO 2005-2012) a provider of information extraction technology to the government, and
(iii) Content Savvy Inc (CEO, 2011-2014), which powers content analytics from a range of news, social media, and custom data sources to enhance business decision making. Content Savvy was acquired by Smart Focus in 2014.
Dr. Srihari received a B. Math. in Computer Science from the University of Waterloo, Ontario, Canada, and a Ph.D. in Computer Science from the University at Buffalo in 1992 for a dissertation on using collateral text in interpreting photographs.
She was named Best Woman in Technology by Infotech-Niagara for 2015. Previously
she was named to the Women of Accomplishment Legacy Project that identified "outstanding women of the 20th and 21st century of Western New York."
My computer science work has focused on (i) multilingual text mining, (ii) multi-document summarization, (iii) social media mining, and (iv) mining e-learning forums. Some of these are described below:
The work in multilingual text mining is focused on languages that are less commonly taught (LCTL). These languages are characterized by the lack of critical resources such as electronic dictionaries, annotated corpora (for training machine learning modules) and other rich resources used in English NLP such as WordNet. We adapted resources from other languages to automatically generate critical tools and data resources and used transfer learning to project English semantic role labels (from PropBank) to an Urdu corpus. We also showed how Hindi resources could be adapted for this task. Finally, some work on Urdu sentiment analysis, namely identifying opinion entities has demonstrated the need for different features when compared to English. Another effort was on multilingual text mining for languages such as Chinese, Arabic, Russian and Pashto in an attempt to support intelligence community initiatives.
Since 1997, I have been actively working in information extraction (IE), a discipline that is at the intersection of linguistics and computer science. IE concerns the ability to extract salient information such as entities, relationships between entities, and events from large collections of unstructured text. I have been involved in applications of IE including question-answering-- focusing on a hybrid approach to IE that combines statistical machine learning with grammar and lexicon-based approaches. The Semantex text mining platform is the culmination of over a decade of funded research in this area and has led to numerous publications and invited talks. It is being used by both government and industry in a variety of text-processing applications. The recent focus has been on using semi-supervised learning in order to facilitate domain porting. Special topics I have been pursuing include temporal and location normalization, massive scalability, entity/name disambuiguation, and processing of noisy text such as chat and blog data.
SEMINAR (Fall 2015): Personalizing Web/Social Media
INFORMATION RETRIEVAL (Fall 2015)
Advanced Information Retrieval: Web, Text and Social Media Mining (Spring 2016)
MULTILINGUAL TEXT MINING
Phone: +1 (716) 645-6164 ext. 102