This paper presents guidelines for the annotation of intentional (i.e. non-conventionalized) linguistic metaphors. Expressions that contribute to the same metaphorical image are annotated as a chain, additionally a semantically contrasting expression of the target domain is marked as an anchor. So far, a corpus of ten TEDx talks with a total of 20k tokens has been annotated according to these guidelines. 1.25% of the tokens are intentional metaphorical expressions.

We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or “inked”) notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.