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EM&AI | AI SELF-SERVICE PLATFORM

Extract customer insight with EM&AI NLP

Gain a deeper understanding of customers’ opinions with a robust NLP technology built and continuously improved by our highly-specialized team

WHY EM&AI FOR NLP SOLUTION?

why-NLP-high-accuracy

High Accuracy Of NLP Model

Specific-domain machine learning models built to meet the separately demand of the business with a high of accuracy

Discover The Customer Insight

Extract insight strongly by using a huge amount of data obtained from NLP models such as entity recognition, sentiment analysis, keyword extraction, labeling keyword and summarizing topic

Extract The Important Information

Identify entities within documents (emails, chat, social media,etc) and label them based on domain-specific keywords or phrases

Easy To Customize

An easy-to-use platform helps people create a huge amount of practical data set and customize models easily.

Diversity Of Pre-Built Domain

Pre-built domains built by EM&AI team based on diversity practical knowledge help save the time for NLP training. The prebuilt domains are fully customizable for business

DEMO EM&AI NLP

Natural Language Processing

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Score Magnitude

Natural Language Processing

Please tell me your information such as name, address, phone, email or your license plate number...

Ex: - Mình tên là Nguyễn Trọng Vỹ.
- Mình đang sống ở 122 Lê Duẩn, quận Hải Châu, Đà Nẵng.
- Số điện thoại của mình là 0901 234 567.
Mình tên là Trần Minh Phương.
Sentiment
N/A
Entities
N/A

Natural Language Processing

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KEY FEATURES

Intent Recognition

Uses statistical modeling and Neural Network to train intent recognition model

Negative Intent

Machine learning techniques be used to distinguish negative sentences. By training the intention of negative sentences is negative to the intention of affirmative sentences will lead to a more accurate identification model

Entity Recognition

Save the time to insights with pre-built common entity recognition. Detect and extract hundreds of important information types from unstructured text such as people, places, organizations, date/time, and percentages, etc

Composite Entity

Using composite entities allows extracting multiple entity values that appear together in each sentence of the document

Sentiment Analysis

Combines natural language processing (NLP) and machine learning techniques to assign sentiment scores to target sentences or phrases and classifies sentiment level as positive, negative or neutral

Keyword, Stopwords, Slangs & Teencodes

Training NLP recognizes keywords, slangs, stopwords, and teen codes help reduce training data interference as well as better recognition of the meaning of user's utterances

Intent Recognition

Uses statistical modeling and Neural Network to train intent recognition model

Negative Intent

Machine learning techniques be used to distinguish negative sentences. By training the intention of negative sentences is negative to the intention of affirmative sentences will lead to a more accurate identification model.

Entity Recognition

Save the time to insights with pre-built common entity recognition. Detect and extract hundreds of important information types from unstructured text such as people, places, organizations, date/time, and percentages, etc

Keywords, Slangs, Teencodes Stopwords

Training NLP recognizes keywords, slangs, stopwords, and teen codes help reduce training data interference as well as better recognition of the meaning of user's utterances

Composite Entity

Using composite entities allows extracting multiple entity values that appear together in each sentence of the document.

Sentiment Analysis

Combines natural language processing (NLP) and machine learning techniques to assign sentiment scores to target sentences or phrases and classifies sentiment level as positive, negative or neutral.

Negative Intent

Distinguish negative sentences by using Machine learning. By training the intention of negative sentences is negative to the intention of affirmative sentences will lead to more accurate identification model.

Intent Recognition

Uses statistical modeling
and Neural Network to train
intent recognition model

Entity Recognition

Save the time to insights with pre-built common entity recognition. Detect and extract hundreds of important information types from unstructured text such as people, places, organizations, date/time, and percentages, etc

Keywords, Slans, Teencodes, Stopwords

Training NLP recognizes keywords, slans, stopwords, and teen codes help reduce training data interference as well as better recognition of the meaning of user's utterances

Composite Entity

Using composite entities allows extracting multiple entity values that appear together in each sentence of the document.

Sentiment Analysis

Combines natural language processing (NLP) and machine learning techniques to assign sentiment scores to target sentences or phrases and classifies sentiment level as positive, negative or neutral.

USE CASES

Machine Translation

Spell Checking

Social Listening

Machine Translation

Spell Checking

Social Listening

Market Intelligence

Virtual Assistant

Summarization

Market Intelligence

Virtual Assistant

Summarization

DRIVE YOUR BUSINESS SUCCESS WITH
EM & AI SOLUTION!

DRIVE YOUR
BUSINESS SUCCESS WITH
EM & AI SOLUTION!

Contact Me on Zalo