Determining the intended sense of words in text – word sense disambiguation (WSD) – is a long-standing problem in natural language processing. Recently, researchers have shown promising results using word vectors extracted from a neural network language model as features in WSD algorithms. However, a simple average or concatenation of word vectors for each word in a text loses the sequential and syntactic information of the text. In this paper, we study WSD with a sequence learning neural net, LSTM, to better capture the sequential and syntactic patterns of the text. To alleviate the lack of training data in all-words WSD, we employ the same LSTM in a semi-supervised label propagation classifier. We demonstrate state-of-the-art results, especially on verbs.

In the design of critical systems and software, validation and verification (VV a subset must be chosen that maximizes the chances of mission success by reducing risk while meeting budget constraints. By explicitly modeling the contributions that various V&V activities make to reducing risks, and the costs of these activities, we are able to convert this to a classical optimization problem. We then use search, clustering and visualization algorithms to examine the large space of options.

Acquisition of "quantitative" models of sufficient accuracy to enable effective analysis of requirements tradeoffs is hampered by the slowness and difficulty of obtaining sufficient data. "Qualitative" models, based on expert opinion, can be built quickly and therefore used earlier. Such qualitative models are nondeterminate which makes them hard to use for making categorical policy decisions over the model. The nondeterminacy of qualitative models can be tamed using "stochastic sampling" and "treatment learning". These tools can quickly find and set the "master variables" that restrain qualitative simulations. Once tamed, qualitative modeling can be used in requirements engineering to assess more options, earlier in the life cycle

LIA