NLP (Natural Language Processing)

NLP (Natural Language Processing) –

Introduction  –

Natural language processing (NLP) is the domain of various disciplines related to understanding  indigenous languages and using them to facilitate human-computer communication. Natural  languages are complex in nature and many NLP functions are not well-structured to achieve  mathematically accurate algorithmic solutions. With the occurrence of large size of data, data ,driven approaches to NLP problems introduced a new paradigm, in which the complexity of  the problem domain is effectively managed through large data sets to create simpler but more  advanced models. 

Indigenous language processing – a computer activity in which computers are required to  analyse, understand, modify, or produce native language. This includes automation of any kind  of language, activities, or communication methods, such as conversation, books, reading,  writing, pronunciation, publishing, translation, lip reading, and so on. Indigenous language  processing is also the name of the branch of computer science, artificial intelligence, and  linguistics related to computer-assisted communication in the use of natural languages (s) of  all kinds, including but not limited to speech, printing, writing, and signing. 

Natural Language Processing (NLP) is a multidisciplinary field whose mission is to analyse and  understand human languages. Indigenous languages are used in two ways: written and spoken.  Text and speech are ways to use written and spoken languages, respectively. 

NLP discusses issues in official ideas related to language knowledge and the NLP used focuses  on the practical results of modelling an indigenous language with the aim of building software  that provides improved human machine interaction. 

NL comprehension involves the transformation of human language, either input speech  (acoustics / phonology) or user-written text. Automatic translation involves the translation of  text from one language to another. Summary of text involves the production of summaries of  texts that cover important information in the text (s), taking into account students’ interests. 

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Structures used in natural language processing  –

Corpus – a data center, optionally marked (for example, by marking part of speech), provides  real-world samples for analysis and comparison. 

• Text corpus – a set of large and organized texts, today commonly stored and processed  electronically. They are used to perform mathematical analysis and to evaluate a hypothesis,  to assess events or to confirm grammatical rules within a particular subject (or domain). 
• Speech corpus – a website for audio and video files. In speech technology, speech corpora is  used, among other things, to create acoustic models (which may be used with a speech  recognition engine). In Linguistics, spoken organizations are used for phonetic research,  dialogue analysis, dialectology and other fields.

Applications of NLP to Mental Health:  

1. Corpus: as one of the topics in NLP, corpus has a text section. The most common companies  record or report (electronic health records [EHRs], Psychological Reports, and Sensory  Reports), social media (Reddit, Twitter, etc.), or patient interviews. 
2. Corpus processing: depending on the type of corpus, one can extract medical terms and  match them with different identifiers of the medical language system (UMLS) (CUIs) or process  text blocks in native language and perform specific searches (e.g., suicide-related terms) . 

NLP is important in psychiatry because language-based deficiency is a common symptom of  depression, behavioural disorders, ASD, personality disorders, and schizophrenia]. It can  provide insight into a person’s mental and emotional health, their use of narrative, imaginative,  structured speech patterns, and their way of life, especially their level of education, 

socioeconomic status, living conditions, and cultural background, all of which are psychological  condition examination. 

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Steps involved in Natural language processing  –

Tokenization: 

Token making is a method of dividing a piece of text into smaller units called tokens. This can  be done at the sentence level or at the word level. 

Text cleaning:  

This section removes the words and objects from the text database to help improve the  efficiency of the machine learning model. Numbers, capital letters, punctuation marks,  abbreviations, individual quotes will be extracted from text data. The process of clearing the  text is done using common expressions

POS tagging:  

POS marking is the task of labelling each word in a sentence and part of it in the correct  expression. Parts of speech include nouns, verbs, adverbs, adjectives, pronouns, conjunctions  and their clauses. 

Stop words:  

These words do not add a lot to the meaning of the text. Stop words are very common in the  language. Stop removal should be done after tokens are made. Nltk offers shortcuts in many  different languages such as Danish, German, English and more. Some of the benefits of  removing shortcuts are: 

1. The data set size decreases as the shortcuts are removed and the model training time  decreases. 
2. The termination of the set name will theoretically help to maximize performance, as there  are fewer and more important tokens left. This can improve the accuracy of the separation. 

Lematization: 

This reduces the wording by making sure that the root of the word is in the language. It helps  to find the right words in the law as well. Nltk provides WorldNet Lemmatizer using WorldNet  Database to search for word lemmas.

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