Applied Sciences Free Full-Text Quantum Natural Language Processing: Challenges and Opportunities

Syntactic Ambiguity exists in the presence of two or more possible meanings within the sentence. Discourse Integration depends upon the sentences that proceeds it and also invokes the meaning of the sentences that follow it. Chunking is used to collect the individual piece of information and grouping them into bigger pieces of sentences. Microsoft Corporation provides word processor software like MS-word, PowerPoint for the spelling correction.

This allows algorithms to understand and sort data found in customer feedback forms. Lenddo applications are helping lenders better assess applicants, meaning that millions of more people are able to safely and responsibly access credit. While most NLP applications can understand basic sentences, they struggle to deal with sophisticated vocabulary sets. Natural language processing uses technology and big data and sophisticated algorithms to simplify this process.

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Natural Language Processing APIs allow developers to integrate human-to-machine communications and complete several useful tasks such as speech recognition, chatbots, spelling correction, sentiment analysis, etc. The Linguistic String Project-Medical Language Processor is one the large scale projects of NLP in the field of medicine [21, 53, 57, 71, 114]. The LSP-MLP helps enabling physicians to extract and summarize information of any signs or symptoms, drug dosage and response data with the aim of identifying possible side effects of any medicine while highlighting or flagging data items [114]. The National Library of Medicine is developing The Specialist System [78–80, 82, 84]. It is expected to function as an Information Extraction tool for Biomedical Knowledge Bases, particularly Medline abstracts. The lexicon was created using MeSH (Medical Subject Headings), Dorland’s Illustrated Medical Dictionary and general English Dictionaries.

There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. In the beginning of the year 1990s, NLP started growing faster and achieved good process accuracy, especially in English Grammar. In 1990 also, an electronic text introduced, which provided a good resource for training and examining natural language programs.

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There was a widespread belief that progress could only be made on the two sides, one is ARPA Speech Understanding Research (SUR) project (Lea, 1980) and other in some major system developments projects building database front ends. The front-end projects (Hendrix et al., 1978) [55] were intended to go beyond LUNAR in interfacing the large databases. In early 1980s computational grammar theory became a very active area of research linked with logics for meaning and knowledge’s ability to deal with the user’s beliefs and intentions and with functions like emphasis and themes. Research being done on natural language processing revolves around search, especially Enterprise search. This involves having users query data sets in the form of a question that they might pose to another person. The machine interprets the important elements of the human language sentence, which correspond to specific features in a data set, and returns an answer.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly [newline]interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the [newline]most exciting work published in the various research areas of the journal. Natural language processing is an increasingly common intelligent application. Cardiff University and Charles III University of Madrid researchers have developed an AI system named VeriPol.

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