<p>This question is very old.  Lot of developments were happened in NLP area in last 7 years. </p>

<p><a href="https://en.wikipedia.org/wiki/Convolutional_neural_network" rel="nofollow noreferrer">Convolutional_neural_network</a> and <a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow noreferrer">Recurrent_neural_network</a> evolved during this time. </p>

<p><em>Word Embeddings:</em>  Words appearing within similar context possess similar meaning. Word embeddings are pre-trained on a task where the objective is to predict a word based on its context.</p>

<p><a href="https://i.stack.imgur.com/ynzh8.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ynzh8.png" alt="Word embeddings"></a></p>

<p>CNN for NLP:</p>

<ol>
<li><p>Sentences are first tokenized into words, which are further transformed into a word embedding matrix (i.e., input embedding layer) of d dimension. </p></li>
<li><p>Convolutional filters are applied on this input embedding layer to produce  a feature map.</p></li>
<li><p>A max-pooling operation on each filter obtain a fixed length output and reduce the dimensionality of the output.</p></li>
</ol>

<p><a href="https://i.stack.imgur.com/BekQ1.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/BekQ1.png" alt="CNN for NLP"></a></p>

<p>Since CNN had a short-coming of not preserving long-distance contextual information, RNNs have been introduced. </p>

<p>RNNs are specialized neural-based approaches that are effective at processing sequential information.</p>

<p>RNN memorizes the result of previous computations and use it in current computation. </p>

<p><a href="https://i.stack.imgur.com/vuGKe.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/vuGKe.png" alt="RNN for NLP"></a></p>

<p>There are few variations in RNN - Long Short Term Memory Unit (LSTM) and Gated recurrent units (GRUs) </p>

<p>Have a look at below resources:</p>

<p><a href="https://medium.com/dair-ai/deep-learning-for-nlp-an-overview-of-recent-trends-d0d8f40a776d" rel="nofollow noreferrer">deep-learning-for-nlp</a></p>

<p><a href="https://arxiv.org/pdf/1708.02709.pdf" rel="nofollow noreferrer">Recent trends in deep learning paper</a></p>
Support Vector Machine or Artificial Neural Network for text processing?
<p>We need to decide between Support Vector Machines and Fast Artificial Neural Network for some text processing project.</p>

<p>It includes Contextual Spelling Correction and then tagging the text to certain phrases and their synonyms.</p>

<p>Which will be the right approach? Or is there an alternate to both of these... Something more appropriate than FANN as well as SVM?</p>