Top Research Papers on NLP

Whether chest CT scans performed routinely during emergency room (ER) visits for general medical conditions could be used to identify patients with incidental lung cancers and NLP software was used to review all CT scans obtained.

The research and development work preceding the piloting of precisely such a tool, capable not only of identifying the typical errors in L2 student writing, but also of making adequate corrections, is described.

This is one of the pioneering attempts to monitor the whole process of cryo-EM image processing using JI Prolog.

This NLP algorithm is a scalable tool that uses unstructured EHR data to capture breast cancer recurrence, distinguishing local from distant recurrence and identifying metastatic site and may facilitate analysis of large datasets and correlation of outcomes with metastatic site.

A novel Natural Language Processing (NLP) framework for embedded systems to analyze and automatically extract verification aspects from the textual design requirements to considerably simplify and accelerate the development of verification assertions.

The intended purpose of NLP4All is to help teachers facilitate learning with and about NLP, by providing easy-to-use interfaces to NLP-methods, data, and analyses, making it possible for non- and novice-programmers to learn NLP concepts interactively.

Elaborazione delle richieste in linguaggio naturale (NLP) nella’ambito dell’enterprise search and degli agenti conversazionali.

The study’s findings demonstrate that NLP significantly improves the accuracy and efficiency of knowledge retrieval by automating the processing of natural language data, which allows better access to tacit knowledge, supporting more informed decision-making in libraries.

The potential utility of NLP to analyze ICs in order to provide insight into patient preferences and decision-making is demonstrated and recommended that multidisciplinary clinics consider including an unbiased specialist to present treatment options and that future decision tools for AS include quantitative data regarding outcomes after AS.

NLP models can be developed and used to enrich structured RWD data by extracting information from unstructured documents thus significantly improving the utility of this data for downstream applications.

A large number of the registries for surveillance, Epidemiology, and End Results (SEER) registries lack information on the Epidermal Growth Factor Receptor (EGFR) mutation and Anaplastic Lymphoma Kinase (ALK) gene ...

NLP can help identify organ specific metastases from pathology reports, such as breast cancer associated-lung metastases, which can then facilitate observational, translational, and clinical research to characterize and address challenges posed by this common site of breast cancer recurrence.

This manuscript reviews general technologic approaches to NLP at a level hopefully understandable by clinical radiologists, discusses recent advancements in NLP techniques, and discusses current and potential applications of NLP in radiology.

This systematic review examines natural language processing approaches applied to aviation safety-related domains and uncovers future opportunities to leverage NLP models to facilitate the safety and efficiency of the aviation system.

The challenges being faced by NLP researchers for Indian Language Conversions are discussed, which require deep knowledge of statistics, computer language, and linguistics.

This paper selected five various Turkish NLP research problems as sentiment analysis, cyberbullying identification, text classification, emotion recognition and spam detection from the literature, and compared the empirical performance of BERT with the baseline ML algorithms.

This paper delves into the exploration of utilizing the knowledge derived from NLP for text-based EHRs, including an extensive list of the advantages, obstacles, and optimal approaches in the most recent NLP language models, along with real-world instances of NLP applications.

An NLP‐based feature extraction technique in Deep‐Learning models to predict BBB permeability is developed and can be used for the early screening of CNS drugs.

It is concluded that adopting NLP in AML compliance can lead to substantial efficiency gains, improved accuracy in identifying suspicious activities, and reduced compliance costs, however, addressing technical and regulatory challenges is crucial for maximizing these benefits.

A model from Natural Language Processing (NLP) was firstly employed to learn and perform the task of intermediate prediction, which is served as a language translation task, providing an efficient instruction for mechanism understanding.