  In this work\footnote {This work was supported in part by the National
Science Foundation under grant IIS-1212948.}, we present a method to represent
a video with a sequence of words, and learn the temporal sequencing of such
words as the key information for predicting and recognizing human actions. We
leverage core concepts from the Natural Language Processing (NLP) literature
used in sentence classification to solve the problems of action prediction and
action recognition. Each frame is converted into a word that is represented as
a vector using the Bag of Visual Words (BoW) encoding method. The words are
then combined into a sentence to represent the video, as a sentence. The
sequence of words in different actions are learned with a simple but effective
Temporal Convolutional Neural Network (T-CNN) that captures the temporal
sequencing of information in a video sentence. We demonstrate that a key
characteristic of the proposed method is its low-latency, i.e. its ability to
predict an action accurately with a partial sequence (sentence). Experiments on
two datasets, \textit{UCF101} and \textit{HMDB51} show that the method on
average reaches 95\% of its accuracy within half the video frames. Results,
also demonstrate that our method achieves compatible state-of-the-art
performance in action recognition (i.e. at the completion of the sentence) in
addition to action prediction.
