Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. 

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Natural language processing: state of the art, current trends and challenges

A General Natural-language Text Processor for Clinical Radiology

Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.

Natural Language Processing: State of The Art, Current Trends and Challenges

PURPOSE: To evaluate translation of chest radiographic reports by using natural language processing and to compare the findings with those in the literature. MATERIALS AND METHODS: A natural language processor coded 10 years of narrative chest radiographic reports from an urban academic medical center. Coding for 150 reports was compared with manual coding. Frequencies and co-occurrences of 24 clinical conditions (diseases, abnormalities, and clinical states) were estimated. The ratio of right to left lung mass, association of pleural effusion with other conditions, and frequency of bullet and stab wounds were compared with independent observations. The sensitivity and specificity of the system’s pneumothorax coding were compared with those of manual financial coding. RESULTS: The system coded 889,921 reports on 251,186 patients. On the basis of manual coding of 150 reports, the processor’s sensitivity (0.81) and specificity (0.99) were comparable to those previously reported for natural language processi...

Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports

Evaluating natural language processing (NLP) systems in the clinical domain is a difficult task which is important for advancement of the field. A number of NLP systems have been reported that extract information from free-text clinical reports, but not many of the systems have been evaluated. Those that were evaluated noted good performance measures but the results were often weakened by ineffective evaluation methods. In this paper we describe a set of criteria aimed at improving the quality of NLP evaluation studies. We present an overview of NLP evaluations in the clinical domain and also discuss the Message Understanding Conferences (MUC) [1-4]. Although these conferences constitute a series of NLP evaluation studies performed outside of the clinical domain, some of the results are relevant within medicine. In addition, we discuss a number of factors which contribute to the complexity that is inherent in the task of evaluating natural language systems.

Evaluating Natural Language Processors in the Clinical Domain

This paper describes the automatic extraction of information from hospital discharge letters. The computer analysis and extraction process, designed to eliminate much of the manual effort required to review patient documents, are presented.

The application of natural-language processing to healthcare quality assessment.

The syntax and semantics of clinical narrative across Indo-European languages are quite similar, making it possible to envison a single medical language processor that can be adapted for different European languages. The Linguistic String Project of New York University is continuing the development of its Medical Language Processor in this direction. The paper describes how the processor operates on English and French.

A Medical Language Processor for Two Indo-European Languages.

The clinical data contained in narrative patient documents is made available via grammatical and semantic processing. Retrievals from the resulting relational database tables are matched against a set of clinical descriptors to obtain clinical profiles of the patients in terms of the descriptors present in the documents. Discharge summaries of 57 Dept. of Digestive Surgery patients were processed in this manner. Factor analysis and discriminant analysis procedures were then applied, showing the profiles to be useful for diagnosis definitions (by establishing relations between diagnoses and clinical findings), for diagnosis assessment (by viewing the match between a definition and observed events recorded in a patient text), and potentially for outcome evaluation based on the classification abilities of clinical signs.

TEXTINFO: a tool for automatic determination of patient clinical profiles using text analysis.

The Linguistic String Project-Medical Language Processor, a system for computer analysis of narrative patient documents in English, is being adapted for French Lettres de Sortie. The system converts the free-text input to a semantic representation which is then mapped into a relational database. Retrievals of clinical data from the database are described.

Medical Language Processing for Knowledge Representation and Retrievals.

Controlled Vocabularies, Indexing and Medical Language Processing. Medical Language Processing: Medical Language Processing for Knowledge Representation and Retrievals

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