Post by juthi52943 on Dec 26, 2023 4:49:15 GMT -5
Far cry from the richness and depth of human language. Since machine language and human language are significantly different, machine learning must be used to train the NER model. This is done by using predefined datasets containing the named entity categories you choose.
For example, in the scenario above, the Job Function Email List entity categories date , person , location , profession , and organization have been predefined. How does NER work? Once trained, NER models use a two-step process to mimic the way humans read. First, the model identifies a named entity, then it classifies or categorizes that entity. Some NER systems use word vectors to improve speed and accuracy.
Word vectors represent words as numbers, but instead of simply assigning a number to each word, word vectors generate numerical representations in decimal format on a number of dimensions, such as frequency of occurrence in a variety of contexts. The result ? Similar words have numbers very close to each other, which allows the NER model to find similar words quickly and accurately.
For example, in the scenario above, the Job Function Email List entity categories date , person , location , profession , and organization have been predefined. How does NER work? Once trained, NER models use a two-step process to mimic the way humans read. First, the model identifies a named entity, then it classifies or categorizes that entity. Some NER systems use word vectors to improve speed and accuracy.
Word vectors represent words as numbers, but instead of simply assigning a number to each word, word vectors generate numerical representations in decimal format on a number of dimensions, such as frequency of occurrence in a variety of contexts. The result ? Similar words have numbers very close to each other, which allows the NER model to find similar words quickly and accurately.
