{"id":1874,"date":"2022-06-14T19:00:00","date_gmt":"2022-06-14T16:00:00","guid":{"rendered":"https:\/\/infoset.app\/blog\/?p=1874"},"modified":"2022-06-07T14:20:21","modified_gmt":"2022-06-07T11:20:21","slug":"what-is-natural-language-processing-where-is-it-used","status":"publish","type":"post","link":"https:\/\/infoset.app\/blog\/what-is-natural-language-processing-where-is-it-used\/","title":{"rendered":"What Is Natural Language Processing & Where Is It Used?"},"content":{"rendered":"\n

Natural language processing, one of the sub-categories of artificial intelligence technologies, works through receiving and processing the human language by machines. Besides the machine language in computer worlds, NLP helps analyze voice texts. So, how can itbe created? <\/p>\n\n\n\n

About Natural Language Processing<\/strong><\/h2>\n\n\n\n

Natural language processing (NLP) encompasses understanding and reproducing the structures of languages such as Turkish, German, French, and English by analyzing them. It is widely used in many areas. According to the research of IBM<\/a>, natural language processing is used in spam detections in emails, in chatbots such as Apple-Siri and Amazon-Alexa, sentiment analysis on social media platforms, and machine translations.<\/p>\n\n\n\n

How Does It Work?<\/strong><\/h2>\n\n\n\n
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The unstructured data added to NLP is brought to a suitable form, defined, and analyzed according to the identified rules with a machine. Machine benefits from algorithms and codes to relational meanings for the studied languages. The data by Sas<\/a> suggest that NLP also benefits from machine learning and deep learning to resolve uncertainty in different languages.<\/p>\n\n\n\n

How to Create Natural Language Processing Pipeline<\/strong><\/h2>\n\n\n\n

Natural language processing consists of a 5-step procedure. These are:<\/p>\n\n\n\n

1) Sentence Segmentation: <\/strong>Voice texts should be analyzed by segmenting the sentences. This way, long texts can be separated into parts quickly.<\/p>\n\n\n\n

2) Word Tokenization: <\/strong>Tokenization is a term that refers to digitalizing transactions. Tokenization of segmented sentences makes processing each sentence easier one by one.<\/p>\n\n\n\n

3) Predicting Parts of Speech for Each Token: <\/strong>Word structures such as verb or subject are predicted after the tokenization. That allows the most accurate prediction by knowing all the roles of words in a sentence. <\/p>\n\n\n\n

4) Text Lemmatization: <\/strong>Lemmatization is the process of finding out the simple form of words. There are differences between a word\u2019s singular and plural versions in many languages. It is possible to figure out the simple forms of words by paragraph and word lemmatization. <\/p>\n\n\n\n

5) Identifying Stop Words: <\/strong>Stop words such as \u201cand, but, like\u201d in texts are filtered and separated.<\/p>\n\n\n\n

Morphologies to Know for Natural Language Processing<\/strong><\/h2>\n\n\n\n
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Among the essential points for NLP is the morphology of the language being worked on. Each language has its unique rules, writing, and pronunciation types. Therefore, it is crucial to categorize language features to work with NLP. While working with NPL, you should know the morphological levels of the relevant language. These levels are:<\/p>\n\n\n\n