NATURAL LANGUAGE PROCESSING (NLP)
Natural language processing Services
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
NLP development methods
The most preferred techniques for NLP are the following:
Parsing (analysis of the sentence into its parts)
Parsing concerns to the examination of the sentence and to 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 some 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.
Emotional analysis (determination of the attitude)
The term is one of the serious problems in the development of natural language processing. 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 established on the 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 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 Tokenizer.
Emotional Analysis
To recognize the sentiment of text. It can vary from bad to good.
The neutral kind is also possible. All these NLP solutions can be applied for the projects.
Languages of NLP
The most used programming language for Natural language processing development is Python. There are 5 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 very 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
This less-popular library Polyglot suggests for 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 Uni. 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.
Our ServReality company guarantees the high-quality NLP development and NLP solutions for the clients.
FAQ
What is NLP?
➢Natural language processing - the sphere of learning that focuses on understanding human speech by machines, NLP examines computer awareness and management of the human speech.
What are the 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.
Where is Deep Learning used?
➢Machine learning and other improved artificial intelligence technologies are applied in cooperative situations and services where the developers and machines interact simultaneously to manage the data..
Where can I look at your cases?
➢You can look at our cases here.
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