Eric Nyberg

TL;DR: The results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.

TL;DR: The proposed method uses a stacked bidirectional Long-Short Term Memory network to sequentially read words from question and answer sentences, and then outputs their relevance scores, which outperforms previous work which requires syntactic features and external knowledge resources.

TL;DR: In this paper, an interactive text editor enforces lexical and grammatical constraints on a natural language subset used by the authors to create their text, which they help disambiguate to ensure translatability, and the resulting translatable source language text undergoes machine translation into any one of a set of target languages, without the translated text requiring any postediting.

TL;DR: It is shown that it is possible to reliably discriminate whether a syntactic construction is meant literally or metaphorically using lexical semantic features of the words that participate in the construction.

TL;DR: In this paper, a natural language processing system utilizes a symbol parsing layer in combination with an intelligent word parsing layer to produce a syntactically or pragmatic correct output sentence or other word message.