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Natural Language Processing is the analysis of human language and the communication between humans and computers. It falls under the field of AI.
What is Natural Language Processing?
Natural Language Processing, also known as NLP, falls within the field of artificial intelligence (AI). This ensures that people and computers can communicate with each other. Humans talk with words, while computers do with signs. NLP thus bridges the gap between the human way of communicating and the way computers do this.
Natural language processing consists of two stages: data preprocessing and algorithm development. The first phase involves preparing and cleaning text data for machines to analyze it. During preprocessing, the data is put into a workable form and the features in the text are emphasized that an algorithm can work with. There are several ways this can be done:
- Splitting the text into smaller units;
- Removing common words from the text;
- Marking words based on part of speech (verb, noun, etc.)
After pre-processing, the algorithm is developed to be able to process them. There are two main types of algorithms that are commonly used, namely: rules-based system and machine learning-based. The first uses carefully designed linguistic rules. The second uses statistical methods.
Tasks of Natural Language Processing
NLP has several tasks to ensure that the human language can be understood by computers. For example, it controls computer programs that can translate text from one language into another. In addition, there are many more tasks that NLP performs. These tasks include:
- Speech recognition;
- Document summary;
- Machine translation;
- Content categorization;
- Contextual extraction;
- Topic discovery and modeling;
- Sentiment analysis.
Natural Language Processing and Text Analysis
NLP goes hand in hand with text analysis. Text analysis is used to explore textual content. In addition, it is also used to derive new variables from raw text that are then used to input other static methods. NLP and text analytics are used together for many applications, including:
- Identifying patterns and clues in emails to help detect spam;
- Classifying content into meaningful topics;
- Identifying key influencers and tracking awareness on specific topics on social media.
NLP in the daily life
The general applications are regularly encountered in daily life. We will cite a few examples.
First of all, everyone knows Alexa or Siri, the virtual assistant of your smartphone. For example, if you ask Siri the question, “What is the capital of Italy?” Siri will tell you that this is Rome. Another example is the spam folder in your email. NLP filters most emails and then puts unwanted email in the spam box. Take a look in your spam box and you will see many similarities between the subject lines. The third example is a search bar on a website. Navigating to a particular page using a search bar is a function of content categorization.
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