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.

Ex Input:
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?

Ex Output:
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


Ex Input:
Maximum Entropy theory is applied to solve Vietnamese word segmentation BIBREF15 , BIBREF22 , BIBREF23 . There are several studies about the Vietnamese word segmentation task over the last decade. Dinh et al. started this task with Weighted Finite State Transducer (WFST) approach and Neural Network approach BIBREF9 . In addition, machine learning approaches are studied and widely applied to natural language processing and word segmentation as well. In fact, several studies used support vector machines (SVM) and conditional random fields (CRF) for the word segmentation task BIBREF7 , BIBREF8 . 
 Question: Which approaches have been applied to solve word segmentation in Vietnamese?

Ex Output:
Maximum Entropy Weighted Finite State Transducer (WFST)  support vector machines (SVM) conditional random fields (CRF)


Ex Input:
The first data batch consists of tweets relevant to blizzards, hurricanes, and wildfires, under the constraint that they are tweeted by “influential" tweeters, who we define as individuals certain to have a classifiable sentiment regarding the topic at hand. For example, we assume that any tweet composed by Al Gore regarding climate change is a positive sample, whereas any tweet from conspiracy account @ClimateHiJinx is a negative sample. The assumption we make in ensuing methods (confirmed as reasonable in Section SECREF2 ) is that influential tweeters can be used to label tweets in bulk in the absence of manually-labeled tweets.  
 Question: What methodology is used to compensate for limited labelled data?

Ex Output:

Influential tweeters ( who they define as individuals certain to have a classifiable sentiment regarding the topic at hand) is used to label tweets in bulk in the absence of manually-labeled tweets.