Abstract As the Natural Language Processing (NLP) and Machine Learning fields mature, the gap between the mathematical equations we write when we model a problem statistically and the manner in which we implement these equations[…]
Abstract Supervised learning technology has led to systems for part of speech tagging, parsing, named entity recognition with accuracies in the high 90%s. Unfortunately, the performance of these systems degrades drastically when they are applied[…]
View Seminar Video Abstract Over the last years finite state transducer technology has found its way in many speech applications, from text processing for synthesizers to the core search algorithms used in speech recognition systems.[…]
View Seminar Video Abstract State-of-the-art NLP models are anything but compact. Parsers have huge grammars, machine translation systems have huge transfer tables, and so on across a range of tasks. With such complexity comes two[…]
Abstract Language understanding is hard because it requires a lot of knowledge. However, the only cost-effective way to acquire a lot of knowledge is by extracting it from text. The best (only?) hope for solving[…]
Abstract A translation is generally taken to be a text that expresses the same meaning as another text in a different language. But the products of the best translators reflects a different, if more illusive,[…]
View Seminar Video Abstract Visual processing of complex natural environments requires animals to combine, in a highly dynamic and adaptive manner, sensory signals that originate from the environment (bottom-up) with behavioral goals and priorities dictated[…]
