Themenschwerpunkt: Natural Language Processing
Land: Vereinigte Staaten
Themen: ux, nlp, custserv, chatbots, ai
I am an NLP Architect on the Enterprise Architecture team at Aspect Software, where I have been centrally involved in the integration of Natural Language Processing components into Aspect’s product suite for customer engagement and the architecting of our Interactive Text Response (chatbot) technology. I have more than 20 years of research experience in the field of Natural Language Processing / Computational Linguistics and pursue diverse interests in human-computer interaction, user modeling, dialogue systems, parsing, and the analysis of non-grammatical text. I hold a PhD in Computer Science, have spent more than a decade as a college professor in the field, and I have been published in multiple international journals, workshops, and conferences in the fields of user-adaptive interaction and Computational Linguistics.
Vorträge / Referenzen:
Presentation at Aspect Customer Experience Conference, May 2017
Do chatbots dream of electric sheep? Are chatbots AI? What is machine learning? What is AI? …I’m confused. Are they the same thing? And what is the difference between NLU, NLP, NLG, and these other acronyms? Do I need to know what a neural network is if I want to give my customers a better way to get answers to their questions? Come learn about Artificial Intelligence and Natural Language Processing and we’ll discuss the industry lingo and how to cut through the hype. Understand what’s possible right now in 2017 and what remains Science Fiction
Keynote Panel, Conversational Interactions Conference, January 2017
Discussion of the real issues in using natural language interaction, e.g., What are the key differences between text and voice interaction? What is the current level of user acceptance? How hard is it to create an effective natural language interaction? What tools and services are available to help do it better and more easily?
Talk presented at the Aspect Customer Experience Conference, May 2016
A look under the hood at the science behind analyzing natural language, why it’s a challenging task, and what is involved in applying NLU technologies toward creating ITR applications.