BERT: A Review of Applications in Natural Language Processing and Understanding
In this review, we describe the application of one of the most popular deep learning-based language models - BERT. The paper describes the mechanism of operation of this model, the main areas of its application to the tasks of text analytics, comparisons with similar models in each task, as well as a description of some proprietary models. In preparing this review, the data of several dozen original scientific articles published over the past few years, which attracted the most attention in the scientific community, were systematized. This survey will be useful to all students and researchers who want to get acquainted with the latest advances in the field of natural language text analysis.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Spoiler Alert: Using Natural Language Processing to Detect Spoilers in Book Reviews
This paper presents an NLP (Natural Language Processing) approach to detecting spoilers in book reviews, using the University of California San Diego (UCSD) Goodreads Spoiler dataset. We explored the use of LSTM, BERT, a…
SentenceA Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing
This review presents a comprehensive exploration of hybrid and ensemble deep learning models within Natural Language Processing (NLP), shedding light on their transformative potential across diverse tasks such as Sentime…
Deep LearningLanguage ModelingLanguage ModellingMachine Translation+8Sentiment Analysis Of Shopee Product Reviews Using Distilbert
The rapid growth of digital commerce has led to the accumulation of a massive number of consumer reviews on online platforms. Shopee, as one of the largest e-commerce platforms in Southeast Asia, receives millions of pro…
Sentiment AnalysisExploring new Approaches for Information Retrieval through Natural Language Processing
This review paper explores recent advancements and emerging approaches in Information Retrieval (IR) applied to Natural Language Processing (NLP). We examine traditional IR models such as Boolean, vector space, probabili…
Argument MiningHate Speech DetectionInformation RetrievalRetrievalAdapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification
Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect-Based Sentiment Analysis (ABSA), which has many applications e.g. in e-commerce, where data and insights from reviews can be leveraged to create value …
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Domain AdaptationGeneral Classification+5