instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
We introduce our proposed diversity, density, and homogeneity metrics with their detailed formulations and key intuitions.
Question: Did they propose other metrics?
answer:
Yes

question:
Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier.
Question: what types of features were used?
answer:
stylometric, lexical, grammatical, and semantic

question:
The Style baseline achieves an Accuracy of 83%, LDA features 89% and a combination of the two gets 90%. However, training on full poems reduces this to 4252%.
Question: Is the outcome of the LDA analysis evaluated in any way?
answer:
Yes