Natural Language Understanding Engine
Ai Suite
Natural Language Understanding Engine LEA
LEA (Language Engineering & Analysis) is a language analysis engine based on machine learning/deep learning that handles text analysis functions such as morpheme analysis, object name recognition, sentence structure analysis, and emotion analysis for non-structured data processing. In addition, natural language processing results are used to provide a higher level of analysis, such as understanding hidden intentions in sentences or identifying question types, which allows the system to understand the intentions for conversation processing and to understand the meaning of questions for analysis and deep Q/A. The natural language understanding engine (LEA) is the basic engine needed for other engines included in AI Suite to operate.
The high-precision language analyzers that make up the natural language engine are applied by machine learning and deep learning (artificial neural network) technologies, and can be used to optimize the quality of each domain through large-scale language resources (large-capacity learning data, dictionaries and rules). Morpheme analyzer provides analysis quality of more than 98%, and syntax analysis and object name extractors provide the world best performance through parallel/distributed processing. LEA engine enables multilingual response in such languages as Korean, English, Japanese, and others, and is a natural language processing engine which could realize semantic analysis, Q&A, and dialogue systems by connecting with knowledge graph.

< Natural Language Understanding Engine – LEA block diagram >
Main Features
High-quality natural language processing based on machine learning and deep learning
Ease of domain application
Meaning identification through the connection with knowledge graph
Main features and specifications
Natural language processing features

Intention analysis feature
Question understanding features
The semantic object is identified by linking the natural language processing results of the input sentence with knowledge graph information. This feature also determines whether it is a declarative sentence or a question, and if it is a question, it categorizes what type of question it is. As such, by analyzing both semantic and syntactic knowledge information included in a sentence and deriving their results, the contents and intentions of the questions can be understood. The question understanding feature is a core function of the cognitive/understanding process for artificial intelligence services as it combines high-level language recognition technology with deep learning-based language / intention / knowledge learning technology with intention analysis.
Dictionary management features
Main engine screen

