Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis PMC

But it’s no longer science fiction; current chatbots that use NLP are no longer distinguishable from humans. That’s because of chatbot software that incorporates natural language processing. Today we have discussed older chatbots, smart chatbots and various elements of NLP. In this series, the previous article was about the use of chatbots in various situation, the current article is about NLP and the future article will be about machine and deep learning.

For example, celebrates, celebrated and celebrating, all these words are originated with a single root word “celebrate.” The big problem with stemming is that sometimes it produces the root word which may not have any meaning. It is used in applications, such as mobile, home automation, video recovery, dictating to Microsoft Word, voice biometrics, voice user interface, and so on. Machine translation is used to translate text or speech from one natural language to another natural language. LUNAR is the classic example of a Natural Language database interface system that is used ATNs and Woods’ Procedural Semantics. It was capable of translating elaborate natural language expressions into database queries and handle 78% of requests without errors. LSTM network is fed by input data from the current time instance and output of hidden layer from the previous time instance.

The core of the modern, smart chatbot.

Similarly, the European Commission emphasizes the importance of eHealth innovations for improved healthcare in its Action Plan [106]. Such initiatives are of great relevance to the clinical NLP community and could be a catalyst for bridging health care policy and practice. ICD-9 and ICD-10 (version 9 and 10 respectively) denote the international classification of diseases [89]. ICD codes are usually assigned manually either by the physician herself or by trained manual coders. In an investigation carried out by the National Board of Health and Welfare (Socialstyrelsen) in Sweden, 4,200 patient records and their ICD-10 coding were reviewed, and they found a 20 percent error rate in the assignment of main diagnoses [90].

However, clinical texts can be laden with medical jargon and can be composed with telegraphic constructions. Furthermore, sublanguages can exist within each of the various clinical sub-domains and note types [1-3]. Therefore, when applying computational semantics, automatic processing of semantic meaning from texts, domain-specific methods and linguistic features for accurate parsing and information extraction should be considered. There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans.

Document-level Analysis

These words have opposite meanings, such as day and night, or the moon and the sun. Two words that are spelled in the same way but have different meanings are “homonyms” of each other. Meaning representation can be used to reason for verifying what is true in the world as well as to infer the knowledge from the semantic representation. The main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related.

Such models include BERT or GPT, which are based on the Transformer architecture. When using static representations, words are always represented in the same way. For example, if the word “rock” appears in a sentence, it gets an identical representation, regardless of whether we mean a music genre or mineral material. The word is assigned a vector that reflects its average meaning over the training corpus.

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