From input text, meanings of the words were learnt automatically and useful representations were generated.

University of Michigan - Ann Arbor

Representational learning is a class of machine learning that focuses on automatically discovering the most appropriate way to represent data. By learning these representations directly from the data, it eliminates the need for manual feature engineering and allows models to effectively process and understand complex inputs.

Representational Learning

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

Speech and Language Processing (3rd ed. draft) 

1. Establishes the most relevant high-level features.

2. Novel representations with high chance that after mapping will look like real 

Representational Learning - Strengths

Autoencoders consist of the two major parts, namely:

1.**encoder**- network compressing high-dimensional input data into lower one.
2.**decoder** - network decompressing representational vector to the original domain. 

Autoencoders can preserve as much information of the original data as possible.
The decoders can be designed to present various nice properties.
Here is the simple diagram for autoencoder:


Autoencoders

Representation Learning in NLP

A deep learning model is a powerful type of machine learning model differentiated by its architecture, which consists of many successive transformations of data chained together from top to bottom. This depth of continuous data transformations allows these models to tackle complex tasks by automatically learning multi-level representations. By doing so, deep learning effectively replaces not only the labor-intensive process of manual feature engineering, but also the shallow models that are typically used at the end of traditional machine learning pipelines.

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