A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Analysis and Application of Language Models to Human-Generated Textual Content
2022
S OCIAL NETWORKS are enormous sources of human-generated content. Users continuously create information, useful but hard to detect, extract, and categorize. Language Models (LMs) have always been among the most useful and used approaches to process textual data. Firstly designed as simple unigram models, they improved through the years until the recent release of BERT, a pre-trained Transformer-based model reaching state-of-the-art performances in many heterogeneous benchmark tasks, such as
doi:10.48676/unibo/amsdottorato/10057
fatcat:gbjtww6jabcoddn5spdpx4z4dq