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 create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps. 
 Question: How many tweats does MMHS150k contains, 150000?
answer:
$150,000$ tweets


question:
After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset. 
 Question: How many sentence transformations on average are available per unique sentence in dataset?
answer:
27.41 transformation on average of single seed sentence is available in dataset.


question:
Methodology ::: Corpus-based Approach
Contextual information is informative in the sense that, in general, similar words tend to appear in the same contexts.  In the corpus-based approach, we capture both of these characteristics and generate word embeddings specific to a domain. Methodology ::: Dictionary-based Approach
In Turkish, there do not exist well-established sentiment lexicons as in English. In this approach, we made use of the TDK (Türk Dil Kurumu - “Turkish Language Institution”) dictionary to obtain word polarities. 
 Question: What word-based and dictionary-based feature are used?
answer:

generate word embeddings specific to a domain TDK (Türk Dil Kurumu - “Turkish Language Institution”) dictionary to obtain word polarities