Identifying Intensity of the Structure and Content in Tweets and the Discriminative Power of Attributes in Context with Referential Translation Machines

We use referential translation machines (RTMs) to identify the similarity between an attribute and two words in English by casting the task as machine translation performance prediction (MTPP) between the words and the attribute word and the distance between their similarities for Task 10 with stacked RTM models. RTMs are also used to predict the Figure 1: RTM depiction: parfda selects interpretants intensity of the structure and content in tweets close to the data using corpora; two MTPPS use in English, Arabic, and Spanish in Task 1 interpretants, training data, and test data to generate where MTPP is between the tweets and the features in the same space; learning and prediction use set of words for the emotion selected from these features as input. Spheres are for feature spaces.