Natural

language processing (NLP)

Natural language processing

The sphere of learning that focuses on understanding human speech by machines, “Natural language processing.” NLP examines computer awareness and management of the human speech.

Functions of NLP

Natural language processing is applied to investigate language. The human-machine relationship can use such current applications as

automated text writing

sentiment analysis

text classification

name recognition

speech fragments recognition

NLP is determined to be a complicated issue in the computer knowledge. Human speech can be perfectly accurate. To figure out human language means to understand the words, but also concepts, the ideas, and the connection they are built upon.

Natural language

The comprehension of human sound interpretation is thought to be a highly complex task. There are many ways of word order in the sentences. The words can have several connotations. Context plays one of the essential elements in the NL.

Syntactic (sentence) and Semantic (meaning) surveys are two main approaches for the natural language research.

NLP development methods

The most preferred techniques for NLP are the following:

Parsing (analysis of the sentence into its parts)

Parsing concerns the examination of the sentence and the creation of a parse tree. The parse tree gives the knowledge about the grammatical interactions of the words according to the structure of the interpretation forms.

Stemming (reduction of the words to their stems)

Stemming comes from the science of morphology. The method helps experts to face certain variants of the words with the same stems and same connotations.

Text segmentation (complete alteration of the text to the significant parts)

Each sentence contains a precise idea or consideration. It is obvious that one program may be created due to one sentence instead of the whole paragraph. If there are the punctuation marks, it makes sense to separate it on the contextual parts.

Relationship separation (determination of the semantic belonging)

The phase includes the process of identification of the semantic links between the named entities.

Sentiment analysis (determination of the attitude)

The term is one of the serious problems in the NLP development. The text should be recognized. The intention should be predicted. The method is often used for the reviews and surveys.

Named entity identification

The main aim of the named entity identification is to encounter and to specify the words with the current realm concepts.

NLP Software

There are some popular Natural Language Processing Software:

Google Cloud Translation API

spaCY

NVivo

IBM SPSS Text Analytics

FuzzyWuzzy

The use of NLP algorithms

Natural Language Processing algorithms are mostly established on machine learning methods. NLP depends on the machine learning to analyze and keep rules and make conclusions. NLP algorithms can be applied for:

Summarization of the text

To sum up the text in order to detect the valuable ideas while paying no attention to the inappropriate one.

The Building of chatbots

Building of chatbots should be done by the Parsey McParseface with tags Point of the Speech.

Generation of keywords

To generate the keyword by applying an AutoTag to find out the themes being within the text.

Identification of the entity

To determine the entity means to discover a person, a location or a company making use of Named Entity Recognition.

Reduction of the words to the stems

To reduce the given words to the stems by applying Porter Stemmer or to divide the text into tokens by applying a Tokenizer.

Sentiment Analysis

To recognize the sentiment of text. It can vary from bad to good. The neutral kind is also an option.

Languages of NLP

The most used programming language for Natural language processing development is Python. Here are 5 of the most suitable Python NLP libraries:

Natural Language Toolkit

The Natural Language Toolkit is able to set up such functions as categorization, tokenization, stemming, identification, analysis, linguistic interpretation. The library is the principal tool for NLP development. It is quite flexible but slow to use.

TextBlob

The TextBlob is an easy interface to use and provides the beginners the valuable data about NLP abilities as reasoning, pos-tagging, and nominal phrase origin.

Polyglot

The less-popular library Polyglot offers customers the detailed analysis and extraordinary language diapason. The system may ask for the use of the chosen command in the command stroke by means of the pipeline instruments.

CoreNLP

The CoreNLP is designed by the Stanford University. The main programming language is Java, but the library supports several languages. The highest efficiency of the library is on the output creation surroundings.

Gensim

The Gensim is the library that concentrates on the meaning likeness among some documents by means of the vector space prototyping and the theme modeling tools.

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