Recurrent neural networks in language processing is used as a network that contains a cycle within its network connections so that the value of X input is directly or indirectly dependent on its own earlier outputs as an input.

University of Texas at San Antonio

RNNs are currently being applied to several applications, including:

 - Temporal analysis—time-series anomaly detection and time-series prediction
 - Computer vision—Image description, video tagging and video analysis
 - NLP—Sentiment analysis, speech recognition, language modeling, machine translation and text generation

Applications of RNN

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

To capture the inherently temporal aspects of language, RNN, unlike other feed-forward neural networks, is used in NLP, allowing us to handle sequences of varying lengths.


Applications include:
- Recurrent neural language models: autoregressive generation
- Sequence labeling: part-of-speech (POS) tagging; named entity recognition; structure prediction
- Sequence Classification
- Elman Networks

Application of RNN in NLP

The RNN-RBM sequence model is a generative model of a sequence of frames $\boldsymbol x^{(t)}$ consisting of an RNN that emits RBM parameters for each time step. This model differs from other approaches because it uses the RNN to emit all of the parametrs of the RBM rather than just varying the bias parameters for each time step. The model is trained by back-propagating the gradient of the loss function through the RNN. The loss function is not applied directly to the RNN outputs, but it is applied to the RBM.

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