Active Learning
Type of machine learning where the model is trained on the data it considers most relevant, by prioritising the instances which are considered to be the most useful, and in that way reduce the data needs.
Annotated corpora
Apart from the pure text, a corpus can also be provided with additional linguistic information, called 'annotation'. This information can be of different nature, such as prosodic, semantic or historical annotation.
Deep Learning Model
A deep learning model is a trained model using a neural network architecture or a set of labelled data that contains multiple layers.
Few-shot learning
Few-shot learning refers to the model’s ability to classify new data when only a limited number of training instances (e.g., 10 to 100) have been provided. As a result, after being exposed to a small amount of prior information, the model improves its performance.
Label (Class)
A pre-defined category of open-ended text.
Model training
Model training is the process of feeding an ML algorithm with data to help identify and learn good examples for all labels (classes) involved.
Neural Language Model
A language model based on neural networks, exploiting their ability to learn distributed representations to reduce the need for huge numbers of training examples when learning highly complex functions.
Pattern
Patters are descriptive sentences which assist the model in understanding and better detecting your label.
Semantic matching
Semantic matching is a technique to determine whether two or more elements have a similar meaning.
Siamese network
A Siamese neural network is an artificial neural network that contains two or more identical subnetworks, where usually only one of them is trained, and all are later used to find the similarity of the inputs by comparing their feature vectors.
Text classification
Text Classification is the process of categorizing text into one or more different classes to organize, structure, and filter into any parameter.
Zero-shot
Zero-shot refers to the model’s ability to classify objects that it has never seen before, or in other words, allow us to assign an appropriate label to a piece of text without having received any training examples before.