What is natural language processing technology

Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and linguistics that focuses on the interactions between computers and human (natural) languages. Natural language processing is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that can realize effective communication between human and computer in natural language. Natural language processing is a science which integrates linguistics, computer science and mathematics. Therefore, the research in this field will involve natural language, that is, people's daily use of the language, so it is closely related to the study of linguistics, but there are important differences. Natural language processing is not a general study of natural language, but the development of a computer system that can effectively achieve natural language communication, especially software systems. Thus it is part of computer science.

Natural language processing technology is all related to the natural language computer processing technology, the purpose is to enable the computer to understand and accept the human nature language input instructions, complete from one language to another language translation function, natural language processing technology research, can enrich the research content of computer knowledge processing, Promote the development of AI technology.

large Fast NLP module is a component of the big fast Big data integration platform, the user references this component can effectively carry on the natural language processing work, such as carries on the article summary, the semantic discrimination as well as enhances the content retrieval accuracy and the validity.

Natural Language processing is now not only a core topic of AI research, but also as a new generation of computer core subject to study. From the knowledge industry point of view, the expert system, database, Knowledgebase, Computer Aided Design system (CAD), computer-aided teaching system (CAI), computerized decision-making system, office automation management system, intelligent robot and so on, all need to use natural language processing, The natural language comprehension system with the ability of discourse comprehension can be used in the fields of machine automatic translation, information retrieval, automatic indexing, automatic summarization, and automatic writing of story novels, all of which can be handled by our tool class Dknlpbase.

Standard participle

method Signature: list<term> standardtokenizer.segment (String txt);

Returns: the word breaker list.

Signature Parameter Description :txt: the statement to be participle.

Example: The following example verifies that paragraph 5 of a word is Afado.

public void Testsegment () throws Exception

{

String Text = " goods and services ";

list<term> termlist = dknlpbase.segment (text);

Assertequals (" Commodities ", Termlist.get (0). Word);

Assertequals (" and ", Termlist.get (1). Word);

Assertequals (" service ", Termlist.get (2). Word);

Text = " Cathay Associates Commentary" Li Shishi VS Afado Second inning "The end is like this ";

Termlist = dknlpbase.segment (text);

Assertequals (" alfa dog ", Termlist.get (5). Word); able to identify " alfa dog "

}

Keyword extraction

method Signature: list<string> Extractkeyword (String txt,int keysum);

return: List of keywords .

Signature Parameter Description :txt: to extract the keyword's statement,keysum to extract the number of keywords

Example: give a word to extract a key word is "programmer".

public void Testextractkeyword () throws Exception

{

String content = " programmer ( English Programmer) is a professional who engages in program development and maintenance. " +

"The programmer is generally divided into program designers and program coders," +

" But the boundaries are not very clear, especially in China. " +

" software practitioners are divided into junior programmers, advanced programmers, Systems " +

" analyst and project manager four categories. ";

list<string> keyword = dknlpbase.extractkeyword (content, 1);

Assertequals (1, keyword.size ());

Assertequals (" programmer ", Keyword.get (0));

}

Phrase extraction

method Signature: list<string> extractphrase (String txt, int phsum);

return: Phrase

Signature Parameter Description : txt: The statementto extract the phrase , phsum the number of phrases

Example: give a paragraph of text, five phrases that can represent a story , the first phrase is an algorithmic engineer .

    into the 21st century, We have entered the Internet as the main symbol of the massive information age, the vast amount of information is mostly in natural language expression. On the one hand, the huge amount of information also provides more material for the computer to learn human language, on the other hand, it also provides a wider application stage for natural language processing. For example, as an important application of natural language processing, search engines gradually become an important tool for people to access information, the emergence of Baidu, Google and other representative of the search engine giants; machine translation also from the laboratory into the ordinary people's homes, Google, Baidu and other companies have provided a large network of data based machine translation and auxiliary translation tools , based on natural language processing of Chinese (input method such as Sogou, Microsoft, Google and other inputs

However, at the same time, we face a grim fact, that is how to effectively use the vast amount of information has become a constraint on the development of information technology a global bottleneck. Natural language processing has inevitably become a new strategic commanding point for the long-term development of information science and technology. At the same time, people realize that simply relying on statistical methods has not been able to quickly and effectively learn language knowledge from the massive data, only at the same time to give full play to the rules-based rationalism method and statistical-based empirical method of the respective advantages, the two complement each other, can be better and faster natural language processing.
&NBSP;&NBSP;&NBSP;&NBSP; natural language processing as a new subject, which is less than a century old, is undergoing rapid development. Reviewing the development of natural language processing, it is not smooth sailing, have a trough, also have an orgasm. And now we are facing new challenges and opportunities. For example, the current web search engine basically still stay in the keyword match, lack of deep-seated natural language processing and understanding. Speech recognition, word recognition, question and answer system, machine translation can only reach a very basic level at present. The road is a long way to repair, natural language processing as a highly interdisciplinary emerging disciplines, whether it is to explore the nature or practical application, in the future there will be unexpected surprises and extraordinary rapid development.  

What is natural language processing technology