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But many business processes and operations leverage machines and require interaction between machines and humans.
That is when natural language processing or NLP algorithms came into existence.
It made computer programs capable of understanding different human languages, whether the words are written or spoken.
NLP makes use of different algorithms for processing languages.
In this article, Ill discuss NLP and some of the most talked about NLP algorithms.
What is NLP?
It helps program machines so that they can analyze and process large volumes of data associated with natural languages.
It gives machines the ability to understand texts and the spoken language of humans.
NLP can also predict upcoming words or sentences coming to a users mind when they are writing or speaking.
As technology has advanced with time, its usage of NLP has expanded.
How Does NLP Work?
NLP is a dynamic technology that uses different methodologies to translate complex human language for machines.
Basically, the data processing stage prepares the data in a form that the machine can understand.
They are concerned with the development of protocols and models that enable a machine to interpret human languages.
Symbolic algorithms leverage symbols to represent knowledge and also the relation between concepts.
Since these algorithms utilize logic and assign meanings to words based on context, you could achieve high accuracy.
However, symbolic algorithms are challenging to expand a set of rules owing to various limitations.
This analysis helps machines to predict which word is likely to be written after the current word in real-time.
The main reason behind its widespread usage is that it can work on large data sets.
However, the major downside of this algorithm is that it is partly dependent on complex feature engineering.
Basically, it helps machines in finding the subject that can be utilized for defining a particular text set.
Latent Dirichlet Allocation is a popular choice when it comes to using the best technique for topic modeling.
It is a quick process as summarization helps in extracting all the valuable information without going through each word.
There are different keyword extraction algorithms available which include popular names like TextRank, Term Frequency, and RAKE.
Each of the keyword extraction algorithms utilizes its own theoretical and fundamental methods.
It is an excellent technique that utilizes triples for storing information.
This algorithm is basically a blend of three things subject, predicate, and entity.
The subject approach is used for extracting ordered information from a heap of unstructured texts.
In this algorithm, the important words are highlighted, and then they are displayed in a table.
Sometimes the less important things are not even visible on the table.
It teaches everything about NLP and NLP algorithms and teaches you how to write sentiment analysis.
With a total length of 11 hours and 52 minutes, this course gives you access to 88 lectures.
This course gives you complete coverage of NLP with its 11.5 hours of on-demand video and 5 articles.
In addition, you will learn about vector-building techniques and preprocessing of text data for NLP.
You will also get to know how you could utilize transformers for cross-lingual transfer learning.
This book also teaches about implementing and evaluating different NLP applications.
