What are the applications of deep learning in natural language processing?

2.Information Extraction: Extract important information from a given text, such as time, place, person, event, cause, effect, number, date, currency, proper noun, etc.

In layman's terms, it's about knowing who, when, why, to whom, what to do, and what the results are. It involves key technologies such as entity recognition, time extraction, and causal relationship extraction.

3.Text mining (or text data mining): including text clustering, classification, information extraction, summarization, sentiment analysis, and visual and interactive expression interfaces for mined information and knowledge. At present, the mainstream technology is based on statistical machine learning.

4.Machine translation: The input of text in the source language is automatically translated to obtain the text in another language.

Depending on the input medium, it can be subdivided into text translation, speech translation, sign language translation, graphic translation, etc. Machine translation has gradually formed a relatively rigorous methodology from the earliest rule-based methods, to the statistics-based methods twenty years ago, and then to today's neural network-based (encoding-decoding) methods.

5.Information Retrieval: Indexing documents at scale.

You can simply assign different weights to the words in the document to create an index, or you can use the techniques of 1, 2, and 3 to create a deeper index. When querying, analyze the input query expression, such as a search term or a sentence, and then find the matching candidate documents in the index, and then sort the candidate documents according to a sorting mechanism, and finally output the document with the highest sorting score.

6.Q&A system: The Q&A system gives an accurate answer to a question expressed in natural language.

Some degree of semantic analysis of natural language query statements is required, including entity linking, relationship recognition, logical expressions, and then going to the knowledge base to find possible candidate answers and finding the best answer through a sorting mechanism.

7.Dialogue system: The system chats with users and completes a certain task through a series of conversations.

It involves technologies such as user intent understanding, general chat engine, question and answer engine, and conversation management. In addition, in order to be contextually relevant, it is necessary to have the ability to have multiple rounds of conversations. At the same time, in order to reflect personalization, it is necessary to develop user portraits and personalized responses based on user portraits.

With the development of deep learning in the fields of image recognition and speech recognition, people also have high hopes for the value of deep learning in NLP. Coupled with the success of Alphago, the research and application of artificial intelligence has become hot. Natural language processing, as cognitive intelligence in the field of artificial intelligence, has become the focus of everyone's attention.

Many companies are entering the field of natural language, hoping to show their talents in the direction of artificial intelligence in the future.

Natural language processing (NLP) is a technology that studies the processing of human language by computers.

Natural language processing includes the following:

1. Natural language processing (NLP) is an important direction in the field of computer science and artificial intelligence. It examines the theories and methods that enable effective communication between humans and computers in natural language. Natural language processing is a science that integrates linguistics, computer science, and mathematics.

2. Therefore, the research in this field will involve natural language, that is, the language that people use on a daily basis, so it is closely related to the study of linguistics, but there are important differences. Natural language processing is not the study of natural language in general, but the development of computer systems, especially software systems, that can effectively realize natural language communication. Thus it is part of computer science.

3. Language is the essential characteristic that distinguishes human beings from other animals. Of all living things, only humans have the ability to speak. Many kinds of human intelligence are closely related to language.

Human logical thinking is in the form of language, and most of human knowledge is also recorded and handed down in the form of language and writing. As such, it is also an important, even core, part of AI.

4. Use natural language to communicate with computers, which has been pursued for a long time. Because it has obvious practical significance, but also important theoretical significance: people can use computers in the language they are most accustomed to, without having to spend a lot of time and energy learning various computer languages that are not very natural and habitual.