We show 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. Our model is constructed using English resources, and we obtain state-of-the-art performance relative to previous work in this language. Using a model transfer approach by pivoting through a bilingual dictionary, we show our model can identify metaphoric expressions in other languages. We provide results on three new test sets in Spanish, Farsi, and Russian. The results support the hypothesis that metaphors are conceptual, rather than lexical, in nature.

The trend toward ubiquitous computing does not represent simply a change in the way people access and use information. In the end it will have a profound effect on the way people access and use services, enabling new classes of services that only make sense by virtue of being embedded in the environment. Ultimately these technologies will lead us to a world of ubiquitous commerce. The prospect of ubiquitous computing, therefore, poses a fundamental question to businesses: What will it mean to conduct commerce in a world where our physical environments are teeming with services?Fundamentally, ubiquitous computing can and will change the way businesses and consumers are able to access each other. Gaining access to customers has been in the past a key challenge for businesses. What if accessing customers disappeared as a problem? Doesn't the rise of ubiquitous computing promise businesses the ability to deliver the right message to the right person at the right time at extremely low cost? Yet, these new and improved ways of reaching customers raise a whole new set of challenges that in many ways are far more complex than issues of cost.

In this paper, we build a corpus of tweets from Twitter annotated with keywords using crowdsourcing methods. We identify key differences between this domain and the work performed on other domains, such as news, which makes existing approaches for automatic keyword extraction not generalize well on Twitter datasets. These datasets include the small amount of content in each tweet, the frequent usage of lexical variants and the high variance of the cardinality of keywords present in each tweet. We propose methods for addressing these issues, which leads to solid improvements on this dataset for this task.

We invesklgated the accuracy of several spectral metrics when used for matchlng In hyperspectral imagery. Spectral matchlng refers to measurhg the similarity among spectra. Two spectra are similar when the spectra distance between them is small and dissimllar otherwise. Spectral matching Is used in many areas such as target detertion and spectra classification. At the heart of spectral matching lay the distance measure used and the threshold value differentiaihg between similar and dhimiiar. Frequent choices for such measures are Spectral Angle (SA) aod Spectra) Information Divergence (SID) since they provide a limited range for threshold values. In addition, two new measures spectral Correlation Angle (SCAj and Spectral Gradient Angle (SCAj are deveIopd Next, we suggest an alternative measure developed by.uslng a Normalized Euclidean Distance (NED). To test the accuracy of the measures we used target information extracted from HYDICE data, and assessment look such as the relatlve spectral dlscrlminatory probability and the relative spectral discriminatory entropy. Experimental results suggest that NED outperk"+ SA. SCA and SCA and Is relatlvdy equlvalent to SID. Given the reduction In computatlon time, NED constilutes an attractive measure to be used in spectra matching. Keywordsiryperspecird images; spectral angle; speed infomation divergence, spectral g d i e n t angle, Euclidean distunce I.

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