As a cutting-edge field of future technological development, artificial intelligence has many sub areas in terms of technological applications, such as deep learning, recommendation engines, computer vision, intelligent robots, natural language processing, real-time speech translation, visual content automatic recognition, etc. Natural language processing is an important direction in the field of artificial intelligence. Overall, artificial intelligence natural language processing is driving the sustained development and rapid breakthroughs of language intelligence, and is increasingly being applied in various industries.
Overall, natural language processing is an application of artificial intelligence that provides a variety of applications for companies that need to quickly and reliably analyze text data. This effectively achieves human-computer interaction and allows for analysis and formatting of large amounts of previously unused data.
From 2008 to now, inspired by the achievements in image recognition and speech recognition, people have gradually begun to introduce deep learning for natural language processing research. From the initial word vectors to word2vec in 2013, the combination of deep learning and natural language processing has reached a climax.
In the past two years, the applications of artificial intelligence natural language processing have included machine translation, information retrieval, and intelligent question answering systems. In terms of intelligent question answering, with the help of artificial intelligence natural language processing, people can accurately analyze the knowledge that users need, and provide personalized and real-time information services to users through interaction. For example, when browsing Zhihu, there will be related Q&A push notifications, hot topics, and ranking of key issues.
The entry of enterprises into the field of artificial intelligence and natural language processing has injected more vitality into the development of related industries, and Baidu is one of them. At present, Baidu has not only achieved fruitful results in natural language processing technology and industrial applications, but also adhered to the concept of open source and win-win cooperation. It has built an open source mass production platform based on the PaddlePaddle deep learning platform, integrating language and knowledge core technologies and diversified scenario solutions, which provides corresponding support for technological innovation of developers.
Looking abroad, in the field of reading comprehension, Stanford University established the Wikipedia based dataset SQUAD through the Amazon crowdsourcing platform in 2016, and Microsoft Asia Research Institute opened the Bing search record based dataset MSMARCO in 2016.
In the medical field, based on cloud platforms, artificial intelligence and natural language processing are used to provide real-time support for key algorithms in patient care processes. Based on integrated electronic health record software, predictive modeling, machine learning, clinical NLP, and artificial intelligence can be directly used to assist healthcare professionals in making real-time decisions while caring for patients.
Analysts have pointed out that in the face of the increasing scale of artificial intelligence natural language processing models and the demand for computer computing power, collaborative innovation at the software and computer hardware levels is of great significance. The core modality of multimodal fusion should be determined by specific tasks, and natural language can be viewed as a symbolic system. But without exploring the actual objects represented by symbols, it will be difficult to learn the underlying essence of symbols.
As humanity enters the era of intelligence, the number of smart devices and various types of data is rapidly increasing. After years of development, the field of natural language processing has made significant progress but also faces many challenges. The main problems include two: semantic understanding (learning of knowledge and common sense) and low resource problems.
Faced with the problem of scarce annotation data resources, such as customer service systems, small language machine translation, domain specific dialogue systems, multi round question answering systems, etc., there is no universal and efficient solution for natural language processing. To overcome the related difficulties, it will take some time.
Natural language processing is the pearl on the hat of artificial intelligence, and machine translation, which fills the language gap, is one of the typical application technologies of natural language processing. The development of machine translation has progressed from the initial use of rule-based systems to statistical machine learning methods, and then to solving various problems such as algorithms and computing power, constantly reaching new heights.
With the rapid development of technology, people will have a deeper understanding of natural language and master knowledge, promoting the greater value of artificial intelligence technology, and providing more impetus for social progress and industrial development.
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