TASK DEFINITION: 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.
PROBLEM: Currently, the following WSD models induced from a text corpus are available: Word senses based on cluster word features. This model uses the cluster words from the induced word sense inventory as sparse features that represent the sense.

Word senses based on context word features. This representation is based on a sum of word vectors of all cluster words in the induced sense inventory weighted by distributional similarity scores.

Super senses based on cluster word features. To build this model, induced word senses are first globally clustered using the Chinese Whispers graph clustering algorithm BIBREF9 . The edges in this sense graph are established by disambiguation of the related words BIBREF11 , BIBREF12 . The resulting clusters represent semantic classes grouping words sharing a common hypernym, e.g. “animal”. This set of semantic classes is used as an automatically learned inventory of super senses: There is only one global sense inventory shared among all words in contrast to the two previous traditional “per word” models. Each semantic class is labeled with hypernyms. This model uses words belonging to the semantic class as features.

Super senses based on context word features. This model relies on the same semantic classes as the previous one but, instead, sense representations are obtained by averaging vectors of words sharing the same class. 
 Question: Do they use a neural model for their task?

SOLUTION: No

PROBLEM: 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?

SOLUTION: 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

PROBLEM: This work presented MVCNN, a novel CNN architecture for sentence classification. It combines multichannel initialization – diverse versions of pretrained word embeddings are used – and variable-size filters – features of multigranular phrases are extracted with variable-size convolution filters.  
 Question: How is MVCNN compared to CNN?

SOLUTION:
MVCNN, a novel CNN architecture for sentence classification. It combines multichannel initialization – diverse versions of pretrained word embeddings are used – and variable-size filters – features of multigranular phrases are extracted with variable-size convolution filters.