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The main difference between RNN and CNN come from their structure of the Neural Network. Due to their specific design, CNNs are more fit for spatial data such as images whereas RNNs are more for temporal data that comes in sequence.
CNNs employ filters within convolutional layers to transform data. Whereas, RNNs reuse activation functions from other data points in the sequence to generate the next output in a series.
The main difference between RNN and LSTM is in terms of which one maintain information in the memory for the long period of time. Here LSTM has advantage over RNN as LSTM can handle the information in memory for the long period of time as compare to RNN. But the question is what is different in LSTM than RNN by which LSTMs are capable of maintaining long term temporal dependencies (remembering information for long period of time).
Machine Learning algorithms don’t understand the textual data rather it understand only numerical data. So the problem is how to convert the textual data to the numerical features and further pass these numerical features to the machine learning algorithms.
As we all know that the raw text stored in some dump repository contains a lot of meaningful information. And in today’s fast changing world, it becomes essential to consider data driven decision than fully rely on experience driven decision.
Named Entity Recognition NER works by locating and identifying the named entities present in unstructured text into the standard categories such as person names, locations, organizations, time expressions, quantities, monetary values, percentage, codes etc. Spacy comes with an extremely fast statistical entity recognition system that assigns labels to contiguous spans of tokens.
Parts of Speech tagging is the next step of the tokenization. Once we have done tokenization, spaCy can parse and tag a given Doc. spaCy is pre-trained using statistical modelling. This model consists of binary data and is trained on enough examples to make predictions that generalize across the language. Example, a word following “the” in English is most likely a noun.
