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There had been both meaningful neural and non-neural approaches to this task. The linguistic features developed in this field can often be transferred to the other areas of text classification.
We shouldn't add this task inside text classification hence readability assessment tends to focus on "measuring" the difficulty of a text. Similar classification prediction models (SVM, HAN, etc.) are frequently used in readability assessment but I believe that the goal is different.
The text was updated successfully, but these errors were encountered:
Good idea. Could this be part of a section on "Automated assessment of written text" or something along those lines, see for example (Yannakoudakis and Briscoe, 2004)?
Readability assessment is traditionally a very handcrafted feature-dependent task. SOTA models tend to be neural network-based models, but more traditional ones use SVM and ~100 linguistic features. There can be some useful insightful insights from the traditional models as well.
There had been both meaningful neural and non-neural approaches to this task. The linguistic features developed in this field can often be transferred to the other areas of text classification.
We shouldn't add this task inside text classification hence readability assessment tends to focus on "measuring" the difficulty of a text. Similar classification prediction models (SVM, HAN, etc.) are frequently used in readability assessment but I believe that the goal is different.
The text was updated successfully, but these errors were encountered: