In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: We used fastText and SVM BIBREF16 for preliminary experiments. 

A: What classification models were used?
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Q: Our features consisted of direct similarity over ConceptNet Numberbatch embeddings, the relationships inferred over ConceptNet by SME, features that compose ConceptNet with other resources (WordNet and Wikipedia), and a purely corpus-based feature that looks up two-word phrases in the Google Books dataset.

A: What features did they train on?
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Q: Algorithm "Online Learning" demonstrates how our CNN model can be trained in a purely online setting. We first initialize the model parameters $\theta _0$ (line 1), which can be a trained model from other disaster events or it can be initialized randomly to start from scratch.

As a new batch of labeled tweets $B_t= \lbrace \mathbf {s}_1 \ldots \mathbf {s}_n \rbrace $ arrives, we first compute the log-loss (cross entropy) in Equation 11 for $B_t$ with respect to the current parameters $\theta _t$ (line 2a). Then, we use backpropagation to compute the gradients $f^{\prime }(\theta _{t})$ of the loss with respect to the current parameters (line 2b). Finally, we update the parameters with the learning rate $\eta _t$ and the mean of the gradients (line 2c). We take the mean of the gradients to deal with minibatches of different sizes. Notice that we take only the current minibatch into account to get an updated model. 

A:
What exactly is new about this stochastic gradient descent algorithm?
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