Natural Language Processing Challenges and Issues: A Literature Review

Abstract

Natural Language Processing (NLP) is the computerized approach to analysing text using both structured and unstructured data. NLP is a simple, empirically powerful, and reliable approach. It achieves state-of-the-art performance in language processing tasks like Semantic Search (SS), Machine Translation (MT), Text Summarization (TS), Sentiment Analyser (SA), Named Entity Recognition (NER) and Emotion Detection (ED). NLP is expected to be the technology of the future, based on current technology deployment and adoption. The primary question is: What does NLP have to offer in terms of reality, and what are the prospects? There are several problems to be addressed with this developing method, as it must be compatible with future technology. In this paper, the benefits, challenges and limitations of this innovative paradigm along with the areas open to do research are shown.

Semantic and Structural Analysis of MIMIC-CXR radiography reports with NLP Methods

“…With the help of NLP tools, the transformation of unstructured text into encoded data becomes feasible, facilitating the automated identification and extraction of information from radiology reports. This process proves valuable in various clinical applications, including diagnostic surveillance, cohort creation, quality assessment, and computer vision labeling [7,8]. Clinicians require access to extensive patient data and medical literature to provide high-quality, quantitative health care.…”