What are the most effective sequence labeling algorithms for NLP model architectures in ML?
Sequence labeling is a common task in natural language processing (NLP) that involves assigning labels to each element of a sequence, such as words, characters, or tokens. For example, sequence labeling can be used for named entity recognition, part-of-speech tagging, or sentiment analysis. In this article, you will learn about some of the most effective sequence labeling algorithms for NLP model architectures in machine learning (ML).
-
Aleksandra PrzegalinskaAssociate Professor and Vice Rector for Innovations and AI @Kozminski University, Harvard CLJE Senior Research…
-
Mohammad AkbariChief Scientific Officer @ Deep Medical | AI Solutions, Healthcare
-
Nisarg BhavsarUG @ IIT KGP | Data @ Swiggy, Mercor, DevRev, NoBroker | Research @ IITB, IIMA, NEU