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.

Q: 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?

A: No
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Q: A meta-research study found only 1 in 9 qualitative papers in Human-Computer Interaction reported inter-rater reliability metrics BIBREF31. Another related area are meta-research and methods papers focused on identifying or preventing low-effort responses from crowdworkers — sometimes called “spam” or “random” responses, or alternatively ”fraudsters” or ”cheaters.” Rates of “self-agreement” are often used, determining if the same person labels the same item differently at a later stage. 
 Question: What are the key issues around whether the gold standard data produced in such an annotation is reliable? 

A:  only 1 in 9 qualitative papers in Human-Computer Interaction reported inter-rater reliability metrics low-effort responses from crowdworkers
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Q: The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences. All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\,224$ words. 
 Question: What is the dataset used as input to the Word2Vec algorithm?

A:
Italian Wikipedia and Google News extraction producing final vocabulary of 618224 words
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