{"id":5076,"date":"2019-10-29T05:51:40","date_gmt":"2019-10-29T09:51:40","guid":{"rendered":"https:\/\/www.freelancinggig.com\/blog\/?p=5076"},"modified":"2021-03-31T11:56:51","modified_gmt":"2021-03-31T15:56:51","slug":"what-is-natural-language-processing-nlp-in-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.freelancinggig.com\/blog\/2019\/10\/29\/what-is-natural-language-processing-nlp-in-artificial-intelligence\/","title":{"rendered":"What is Natural Language Processing (NLP) in Artificial Intelligence"},"content":{"rendered":"

The NPL which is even known as Natural language\nprocessing is an ability of the computer program to clearly understand human\nlanguage about how it is spoken. The NLP is mainly a component of AI or\nartificial intelligence. The development of the NLP applications is quite much\nchallenging process because of the reason that the computers traditionally need\nhumans to simply “speak” them in the programming language which is\nquite much precise, unambiguous as well as highly structured, or even through\nthe limited number of the pronounced voice commands. Moreover, Human speech, on\nthe other hand, is not usually precise — it is quite often ambiguous and\nlinguistic structure may also clearly depend on a various set of complex\nvariables that includes slang, regional dialects as well as social context.<\/p>\n\n\n\n

What is the working of natural language processing?<\/strong><\/em><\/p>\n\n\n\n

Techniques and tools       <\/strong><\/em><\/p>\n\n\n\n

Syntax, as well as the semantic analysis, is key 2\ntechniques that are mainly used with the processing of natural language. The\nsyntax is mainly the arrangement of the words in the sentence for making some\nkind of grammatical sense. Also, NLP uses the syntax to assess the meaning of\nthe language based on the grammatical rules. Also the Syntax techniques are\nmainly used including the parsing which is also known as grammatical analysis\nfor the sentence, also the word segmentation that helps to divide a huge piece\nof the text to units, also the sentence breaking that places the sentence\nboundaries in the huge texts as well as the morphological segmentation that\ndivides words into the groups along with the stemming that helps to divides\nwords through inflection to root the forms.<\/p>\n\n\n\n

However, the semantics mainly involves using as well as\nthe meaning of the behind words. Moreover, NLP also applies the algorithms to\nunderstand the meaning as well as the structure of the sentences. Moreover the\ntechniques which NLP uses with the semantics include the disambiguation of word\nsense that also derives for meaning of the word based on the context, named as\nthe entity recognition that also helps to determines words which may also be\ncategorized in the groups, as well as the generation of natural language that\nwill use a database that could help to determine semantics that are behind\nwords.<\/p>\n\n\n\n

Recent approaches:<\/strong><\/em><\/p>\n\n\n\n

As per the Current approaches to the NLP are mainly based\non deep learning, kind of the AI which helps to examine as well uses patterns\nin the data for enhancing the understanding of the program. Deep models of the\nlearning need the massive amounts of the labeled data that could also help to\ntrain on as well as to identify the most relevant correlations, as well as\nassembling such type of set of big data is one of the key hurdles to the NLP\nrecently.<\/p>\n\n\n\n

Earlier Approach<\/strong><\/em><\/p>\n\n\n\n

Earlier approaches for the NLP involved much more\napproach which are rules-based, where the simple level of the machine learning\nabout the algorithms was told about the words as well as about the phrases to\nlook for the in-text as well as about given specific responses while these\nphrases appeared. On the other hand, deep learning is quite much flexible with\nthe intuitive approach where the algorithms learn to simply identify the intent\nof the speakers from various examples, almost like how does the child will\nlearn the human language.<\/p>\n\n\n\n

Three tools are commonly used for NLP that mainly include\nNLTK, Intel NLP Architect as well as Gensim. The toolkit of NTLK also known as\nthe Natural Language Toolkit is mainly the open-source of the python modules\nwith the different sets of data as well as the tutorials. On the other hand,\nGensim is also the Python library for the topic modeling as well as for\ndocument indexing. Even, Intel NLP Architect is another Python library mentioned\nfor deep learning techniques as well as topologies.<\/p>\n\n\n\n

Different use of natural language processing<\/strong><\/em><\/p>\n\n\n\n

As per the research being done on the natural language\nprocessing that searches and revolves, particularly enterprise search. It\nmainly allows the users to query the sets of data in the form of the question\nwhich might also pose to some other set of the person. The machine interprets\nsignificant for the sentences of human languages, like those which might simply\nhelp to correspond to particular features in the data set as well as it also\nreturns an answer.<\/p>\n\n\n\n

NLP may also be used for interpreting the free text and also make it quite much analyzable. There is also a tremendous amount of information that is well stored in the files of free text, such as medical records of the patients, for instance. Before deep learning that is based on the NLP models, the information was completely inaccessible to the analysis of computer-assisted and could also not be analyzed systematically. However, the NLP permit analysts to simply sift through the huge and the great troves of the free text to search for the relevant information in files.<\/p>\n\n\n\n

As per the Sentiment analysis is also some other primary\nuse case<\/a> for the NLP. By using the sentiment analysis, even the data scientists\nmay also simply assess comments on the social media to check that how the brand\nof business that is performing, for instance, or the review notes from the\nteams of customer service to simply identify areas where do people wish their\nbusiness to perform better. Google as well as some different base of search\nengines with the technology of machine translation on NLP deep learning models\nas it allows the algorithms for reading the text on the webpage, interprets\ntheir meaning as well as translates to some another language.<\/p>\n\n\n\n

Significance of NLP<\/strong><\/em><\/p>\n\n\n\n

The benefit of the natural language for processing may\nalso be seen while considering the following statements. If you use the natural\nlanguage for the processing for search, the program will also help to recognize\ncloud computing is mainly an entity, that the cloud is an abbreviated form for\ncloud computing and SLA is an industry acronym for the agreement of service\nlevel.<\/p>\n","protected":false},"excerpt":{"rendered":"

The NPL which is even known as Natural language processing is an ability of the computer program to clearly understand human language about how it …<\/p>\n","protected":false},"author":3,"featured_media":5077,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1905],"tags":[774,1669,1668],"class_list":["post-5076","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","tag-artificial-intelligence","tag-nlp","tag-nlp-in-artificial-intelligence"],"yoast_head":"\nWhat is Natural Language Processing (NLP) in Artificial Intelligence - Developers, Designers & Freelancers - FreelancingGig<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.freelancinggig.com\/blog\/2019\/10\/29\/what-is-natural-language-processing-nlp-in-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is Natural Language Processing (NLP) in Artificial Intelligence - 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