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:
Just as introduced in sec:introduction, it is intractable to compute similarity exactly, as involving INLINEFORM0 computation. Hence, we consider the monte-carlo approximation: DISPLAYFORM0

where INLINEFORM0 is a list of entity pairs sampled from INLINEFORM1 . We use sequential sampling to gain INLINEFORM6 , which means we first sample INLINEFORM7 given INLINEFORM8 from INLINEFORM9 , and then sample INLINEFORM10 given INLINEFORM11 and INLINEFORM12 from INLINEFORM13 . 
 Question: Which sampling method do they use to approximate similarity between the conditional probability distributions over entity pairs?
answer:
monte-carlo sequential sampling


question:
The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061. 
 Question: Which dimensionality do they use for their embeddings?
answer:
1061


question:
Each classifier is implemented with the following specifications:

Naïve Bayes (NB): Multinomial NB with additive smoothing constant 1

Logistic Regression (LR): Linear LR with L2 regularization constant 1 and limited-memory BFGS optimization

Support Vector Machine (SVM): Linear SVM with L2 regularization constant 1 and logistic loss function

Random Forests (RF): Averaging probabilistic predictions of 10 randomized decision trees

Gradient Boosted Trees (GBT): Tree boosting with learning rate 1 and logistic loss function Along with traditional machine learning approaches, we investigate neural network based models to evaluate their efficacy within a larger dataset. In particular, we explore Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variant models. 
 Question: What learning models are used on the dataset?
answer:

Naïve Bayes (NB) Logistic Regression (LR) Support Vector Machine (SVM) Random Forests (RF) Gradient Boosted Trees (GBT)  Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN)