"Deep Language Understanding"
Dr. James Allen, The University of Rochester
Monday, April 2nd at 5:30 p.m., 118 Psychology
Deep language understanding involves mapping language to its intended meaning in context, using concepts and relations in an ontology that supports knowledge and reasoning. Currently, one would think there was consensus across the field of Computational Linguistics that deep understanding is not possible, and almost all current research in the field focuses on developing new machine learning techniques over large corpora. I will argue that while the machine learning approach is producing significant results, and will continue to do so, it has also served to isolate the field from its original home within Artificial Intelligence. As a result, current natural language work is almost completely divorced from work in reasoning, planning and acting. In this talk I will argue that, contrary to current thought, an effective level of deep understanding is a very viable research area. I will present examples of recent work to support these claims. Interestingly, while I argue that the current statistical paradigm is unlikely to achieve deep understanding, it is also the case that deep understanding will likely only be possible by exploiting the advances in statistical approaches.
Suggested Readings
Extended abstract for Deep Language Understanding. [.pdf]
Allen et al. (2007). PLOW: A Collaborative Task Learning Agent. AAAI.[.pdf]
Allen et al. (2008). Deep Semantic Analysis of Text. Proceedings of Semantics in Text Processing: STEP 2008 Conference Proceedings.[.pdf]