<p>You can definitely concatenate an embedding with other features and feed this as an input to a model.</p>

<p>In the speech domain this was done for example in <a href="https://arxiv.org/pdf/1908.04284.pdf" rel="nofollow noreferrer">Personal VAD</a> where a speaker's embedding was concatenated with other features describing speech to determine whether the target speaker is speaking in the given audio.  </p>

<p>I am pretty sure the same approach can and has been applied to other machine learning application fields than speech (pretty sure I have seen it in NLP but can't come up with any paper right now).</p>

<p>In the end of the day you give your model just extra information. If that information is useless then ideally your model would figure that out and set the corresponding weights to zero. However, in reality you are just making the training task more complex and might end up with a worse model (if the features are not that useful or the model is not complex enough to capture the relation between input and output).</p>

<p>Either way, machine learning (especially deep learning) is partly (or largely) trial and error. Not everything is theoretically well established to the point where someone will tell you that "this will work". If your model can figure out the relation between input and output and learn the appropriate mapping function depends on your dataset, model and choices you make for your training settings. Try it out yourself and see whether it works for you. </p>
Is word embedding + other features possible for classification problem?
<p>My task was to create a classifier model for a review dataset. I have 15000 train observations, 5000 dev and 5000 test. </p>

<p>The task specified that 3 features needed to be used: I used <code>TFIDF</code> (5000 features there), <code>BOW</code> (2000 more features) and the <code>review length</code> (1 more feature). So, for example, my X_train is an array shaped (15000,7001).</p>

<p>I was investigating and I found that word embedding (<code>word2vec</code> especially) could be a nice alternative to BOW. 
My question is, can it be used (can it be put in the same "array" format as my other features?) in addition to my other features? </p>

<p>I did some research on it but didn't quite answered my question. </p>