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?
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
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.
Natural Language Processing
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
