Natural Language Processing (NLP) Complete Guide 2018.

What is AI Chatbot and Natural Language Processing??

Conversational UI’s are basically interface based chatbots, which interact with users via text, voice or any other Natural Language interfaces. Natural language refers to the way we interact with people, and automatic manipulation of it is known as Natural language processing(NLP). NLP allows chatbots to understand your messages and respond accordingly to create meaningful conversations.

Also, the better the graphical user interfaces, the better the user experience will be and engage more users. A good conversational UI is a combination of chat,voice or any natural language interfaces and Graphical user interface tools, which uses NLP to convey messages. The machine learns this patterns and improve itself with more interactions. These all processes are collectively called as “Artificial intelligence.”

So, “AI chatbots” can be defined as a computer program to interact with users using NLP, powered by  machine learning and rely on AI for its functioning

                            

Natural Language Processing (NLP) :

According to Wikipedia, “Natural Language Processing, also known as NLP, is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large amounts of natural language data.”In lamens terms, Natural Language Processing (NLP) is concerned with how technology can meaningfully interpret and act on human language inputs. NLP allows technology such as Amazon’s Alexa to understand what you’re saying and how to react to it. Without NLP, AI that requires language inputs is relatively useless. Computational linguistics also became known by the name of natural language process, or NLP, to reflect the more engineer-based or empirical approach of the statistical methods.The statistical dominance of the field also often leads to NLP being described as Statistical Natural Language Processing, perhaps to distance it from the classical computational linguistics methods.


What Natural Language Processing Does?

Powerful Text Analysis: Google Cloud Natural Language reveals the structure and meaning of text by offering powerful machine learning models in an easy to use REST API. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage.

Insights from your customers:Extract actionable insights on product reception or user experience from customer conversations in email, chat or social media by using entity detection and sentiment analysis.

Multimedia, Multi-lingual Support: Combine the API with our Google Cloud Speech API and extract insights from audio conversations. Use with Vision API OCR to understand scanned documents. Extract entities and understand sentiments in multiple languages by translating text first with Translation API.

Content Classification Relationship Graphs: Classify documents by common entities or 700+ general categories such as News, Technology and Entertainment. Build relationship graphs of entities extracted from news or wikipedia articles, by using signals from the state of the art syntax analysis.

Best of Google Deep Learning models: This API brings to you the same Machine Learning technology that both powers Google’s ability to find specific answers to user questions in Google search and is the language understanding system behind the Google Assistant.

CLOUD NATURAL LANGUAGE FEATURES:

Entity Recognition

Identify entities and label by types such as person, organization, location, events, products and media

Sentiment Analysis

Understand the overall sentiment expressed in a block of text.

Syntax Analysis

Extract tokens and sentences, identify parts of speech (PoS) and create dependency parse trees for each sentence.

Content Classification

Classify documents in predefined 700+ categories.

Integrated REST API

Access via REST API. Text can be uploaded in the request or integrated with Google Cloud Storage.

Multi-Language

Enables you to easily analyze text in multiple languages including English, Spanish, Japanese, Chinese (Simplified and Traditional), French, German, Italian, Korean and Portuguese.

What Can Developers Use Natural Language Processing Algorithms For?

NLP algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences), and making a statical inference. In general, the more data analyzed, the more accurate the model will be.

    • Summarize blocks of text using Summarizer to extract the most important and central ideas while ignoring irrelevant information.
    • Create a chat bot using Parsey McParseface, a language parsing deep learning model made by Google that uses Point-of-Speech tagging.
    • Automatically generate keyword tags from content using AutoTag, which leverages LDA, a technique that discovers topics contained within a body of text.
    • Identify the type of entity extracted, such as it being a person, place, or organization using Named Entity Recognition.
    • Use Sentiment Analysis to identify the sentiment of a string of text, from very negative to neutral to very positive.
  • Reduce words to their root, or stem, using PorterStemmer, or break up text into tokens using Tokenizer

A Few Natural Language Processing Examples

    • Use Summarizer to automatically summarize a block of text, exacting topic sentences, and ignoring the rest.
    • Generate keyword topic tags from a document using LDA (Latent Dirichlet Allocation), which determines the most relevant words from a document. This algorithm is at the heart of the Auto-Tag and Auto-Tag URL microservices.
  • Sentiment Analysis, based on Stanford NLP, can be used to identify the feeling, opinion, or belief of a statement, from very negative, to neutral, to very positive. Often, developers with use an algorithm to identify the sentiment of a term in a sentence, or use sentiment analysis to analyze social media.
Techniques of NLP:

Why choose us?

We as a  Chatbot development company in Raipur NB Digital Technologies has evolved ourselves as a unique and hardworking entity we see all the prospects of every tangible and intangible that we provide to our customers. You can trust our product and services as we are serving it from past 1 decade and we have a highly experienced team who have been working on chatbots from a long time

For pricing and quotations NB Digital Technologies

0 Shares

Leave a Reply

Your email address will not be published. Required fields are marked *

6 + 4 =