AI AGENT utilizing the technical strengths of NER and NLP
NER (Named Entity Recognition) Overview
Object Name Recognition (NER) is a technology for identifying and classifying specific types of proper names (such as people, place names, times, and organization names) in text.
According to the Korea Information and Communication Technology Association, NER is a technique for extracting predefined object names (such as people, places, units, etc.) from documents and classifying them.
e.g.
“Cheol-su [name of person] promised to meet Young-hee [person name] at Seoul Station [name] at 10 o'clock [time].”
The definition of NER is also covered in detail in natural language processing (NLP) research papers. One study describes this as “the process of finding object names in text and classifying them into predefined categories,” and many NLP applications are built based on this.
Types of NER and why they are needed
Types of NER
- Generic nES: A basic object name, such as a person's name, city, or date.
- A specific domain object name (domainIdentified NES): A term specific to a specific industry, such as healthcare and law.
Twigfarm optimizes translation quality by treating common object names as learned algorithms and specific domain object names into predefined glossaries such as translation memories (TMs).
The need for NER
NER plays a key role in translation, search, question answering, and document summarization.
In particular, failure to accurately recognize proper names in machine translation (MT) can not only reduce translation quality, but also have a negative impact on the user experience.
For example, instead of translating “TWIGFARM” as “twig farm,” it's important to correctly recognize it as a company name.
The application of NER increases the consistency and accuracy of translation and provides natural translation results. This results in significant quality improvements, particularly in neural network-based machine translation (NMT).
The role of natural language processing (NLP)
NLP is a technology for processing, understanding, and generating natural language, and encompasses various subtechnologies including NER.
Key NLP applications
- Information search: Quickly search for the desired information in the text.
- Question answering system: The chatbot creates appropriate answers to user questions.
- version: Automatic conversion of multilingual text.
- Article summary: A summary of key points in a long text.
The combination of NER and NLP techniques contributes to understanding the context of the text and providing more advanced information.
Twig Farm's LETR WORKS technology
Using NER and NLP in LETR WORKS
- Strengthening proper name recognition: Accurate processing of proper names by combining learned NER models and translation memories.
- Multilingual translation optimization: Improved text analysis and translation accuracy.
- Optimizing specific industry domains: Apply domain-specific glossary of legal, medical, etc.
Technical advantages
- Advanced algorithms: Application of deep learning-based NER and NLP technology.
- Multimodal AI: Provides more sophisticated results by analyzing not only text but also image and audio data together.
- Scalability: Flexible solutions for different industries and languages.
Why choose LETR WORKS
- Accuracy and reliability: Advanced NER technology minimizes proper names and translation errors.
- Improved user experience: Provides more natural translation and higher consistency.
- Economic efficiency: Save time and money by automating translations.
- Support for various tools: Provides a wide range of functions such as translation, subtitle sync adjustment (SyncSub), and subtitles for the deaf (SDH).
NER and NLP are at the core of modern AI technology, and Twig Farm's LETR WORKS is an optimal solution to improve translation quality and user experience by accurately processing proper names based on this technology. LETR WORKS provides efficiency and accuracy in various domains, and is a competitive translation and content localization tool in the global market.
Editor/Choi Min-woo
