Datasets For Neural Sequence Tagging with the Implementation in TensorFlow and PyTorch

Datasets For Neural Sequence Tagging with the Implementation in TensorFlow and PyTorch

Sequence Tagging is a sort of pattern recognition task that includes the algorithmic task of a categorical tag to every individual from.

In Artificial Intelligence, Sequence Tagging is a sort of pattern recognition task that includes the algorithmic task of a categorical tag to every individual from a grouping of observed values. It consists of various sequence labeling tasks: Part-of-speech (POS) tagging, Named Entity Recognition (NER), and Chunking.

POS-labeling gives a grammatical feature name to each word in a sentence; Named Entity Recognition requires recognizing named elements, similar to individual or association names; chunking targets distinguishing syntactic constituents inside a sentence, similar to the noun or verb phrase.

Here, we will cover the details of datasets used in Sequence Tagging. Further, we will execute these datasets using Tensorflow  and Pytorch  library. 

part of speech pytorch tensorflow

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