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:
Embedding: We developed different variations of our models with a simple lookup table embeddings learned from scratch and using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13 (trained and provided by the authors). 
 Question: What embeddings are used?
answer:
 simple lookup table embeddings learned from scratch using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13


question:
A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks. 
 Question: On what model architectures are previous co-occurence networks based?
answer:
in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window connects only adjacent words in the so called word adjacency networks


question:
Our dataset contains tweets about `ObamaCare' in USA collected during march 2010.  
 Question: What dataset of tweets is used?
answer:

tweets about `ObamaCare' in USA collected during march 2010