# Building an Operational Solution Assistant System for Foreign SMEs in ROK
## 3 Related Research
### Natural Language Processing (NLP) and Natural Language Understanding (NLU) Techniques
#### 3.2.1 Natural Language Processing (NLP)  
In the field of human language technology, Natural Language Processing (NLP) adopting a series of computational techniques for the analysis and/or representation of language-based expressions [13] plays an essential part in machine translation, question answering, text summarization, topic modeling, as well as opinion mining, IR and IE. Such an NLP has been used in this research to identify and exclude stop-words, or for the text tokenization (e.g., sentence/word tokenization). Being a basic process in NLP [14], a word string is broken into a series of tokens based on a specific protocol. While sentence tokenization separates and lists sentences in a text corpus, word tokenization breaks up the words in a sentence, based on the spaces, commas, periods and/or carriage return characters. In these techniques, removal of high-frequency words of negligible semantic value (stop-words removal) is an essential step. Generally, word tokens can be separatedby blank spaces, and sentence tokens, by stops. Removing stop words is an important step in NLP text processing. It involves filtering out high-frequency words that add little or no semantic value to a sentence. As an offshoot of Artificial Intelligence, NLP focuses on studying the interaction between computers and natural human languages in the form of speech or text. NLP can be divided into two large, not completely independent branches: speech processing and text processing. Speech processing focuses on the research and development of algorithms and computer programs that process human language in the form of voices (audio data). Important applications of speech processing include speech recognition and speech synthesizer. Whereas speech recognition is to convert language from speech to text, synthesized speech converts language from text to speech. Text processing focuses on text data analysis. Important applications of text processing include information search and retrieval, machine translation, automatic text summary or automatic spelling check. Text processing is sometimes further divided into two smaller branches including text understanding and text birth [14]. In this research, we focused on collected and processed Text data. Text processing consists of the following four main steps:  
* Diagnostic analysis: The identification, analysis and description of the structure of a hieroglyph in a given language and other linguistic units, such as root word, verb, affix, subclass, etc. In Vietnamese language processing, two typical problems in this section are word segmentation and part-of-speech tagging. * Parsing: Process of parsing a series of symbols in the form of natural language or computer language according to formal grammar. Formal grammar commonly used in the parsing of natural languages includes Context-free Grammar (CFG), Combinatory categorical grammar (CCG) and Secondary Grammar Dependency grammar-DG. Input to the parse is a sentence consisting of a series of words and their type tag, and output is a parse tree representing the sentence's syntactic structure. * Semantic analysis: Process of relating semantic structure, from the phrase, clause, sentence and paragraph level to the whole article level, to their independent meanings. In other words, this is to find out the semantics of the verbal input. Semantic analysis has two levels: Semantic lexical expression, which expresses the meanings of the component words and distinguishing the meaning of words; and Component semantics, which refers to the way words combine to form broader meanings. * Discourse analysis: Text analysis considering the relationship between language and context-of-use. Therefore, discourse analysis is performed at the paragraph level or whole text rather than just analysis at the sentence level alone. There are many different approaches used to process natural language in NLP, and they fall roughly into four categories: symbolic, statistical, connectionist and hybrid.