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.

Input: Consider Input: For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to.  
 Question: Along which dimension do the semantically related words take larger values?

Output: dimension corresponding to the concept that the particular word belongs to


Input: Consider Input: We compare our model with the following state-of-the-art joint entity and relation extraction models:

(1) SPTree BIBREF4: This is an end-to-end neural entity and relation extraction model using sequence LSTM and Tree LSTM. (2) Tagging BIBREF5: This is a neural sequence tagging model which jointly extracts the entities and relations using an LSTM encoder and an LSTM decoder. (3) CopyR BIBREF6: This model uses an encoder-decoder approach for joint extraction of entities and relations. (4) HRL BIBREF11: This model uses a reinforcement learning (RL) algorithm with two levels of hierarchy for tuple extraction. (5) GraphR BIBREF14: This model considers each token in a sentence as a node in a graph, and edges connecting the nodes as relations between them. (6) N-gram Attention BIBREF9: This model uses an encoder-decoder approach with N-gram attention mechanism for knowledge-base completion using distantly supervised data. 
 Question: What is previous work authors reffer to?

Output: SPTree Tagging CopyR HRL GraphR N-gram Attention


Input: Consider Input: Recommendation diversity. As defined in BIBREF18 , we calculate recommendation diversity as the average dissimilarity of all pairs of tags in the list of recommended tags. 
 Question: how is diversity measured?


Output: average dissimilarity of all pairs of tags in the list of recommended tags