Electronically available multi-modal data (primarily text and meta-data) is unprecedented in terms of its volume, variety, velocity, (and veracity). The increased interest and investment in cognitive computing for building systems and solutions that enable and support richer human-machine interactions presents a unique opportunity for novel statistical models for natural language processing. In this talk, I will describe a journey at IBM during the past three decades in developing novel statistical models for NLP covering statistical parsing, machine translation, and question-answering systems. Along with a discussion of some of the recent successes, I will discuss some difficult challenges that need to be addressed to achieve more effective cognitive systems and applications.
CITATION STYLE
Roukos, S. (2014). IBM cognitive computing - An NLP renaissance! In EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 2–13). Association for Computational Linguistics (ACL).
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