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.

The dataset consists of a set of polysemous words: 20 nouns, 20 verbs, and 10 adjectives and specifies 20 to 100 contexts per word, with the total of 4,664 contexts, drawn from the Open American National Corpus. Given a set of contexts of a polysemous word, the participants of the competition had to divide them into clusters by sense of the word. The contexts are manually labelled with WordNet senses of the target words, the gold standard clustering is generated from this labelling.

The task allows two setups: graded WSI where participants can submit multiple senses per word and provide the probability of each sense in a particular context, and non-graded WSI where a model determines a single sense for a word in context. In our experiments we performed non-graded WSI. We considered the most suitable sense as the one with the highest cosine similarity with embeddings of the context, as described in Section SECREF9. 
 Question: How are the different senses annotated/labeled? 
The contexts are manually labelled with WordNet senses of the target words

Fenerbahçe We have decided to consider tweets about popular sports clubs as our domain for stance detection.  Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey.  Then, we have annotated the stance information in the tweets for these targets as Favor or Against. For the purposes of the current study, we have not annotated any tweets with the Neither class. 
 Question: How were the tweets annotated?
tweets are annotated with only Favor or Against for two targets - Galatasaray and Fenerbahçe

 After having sentence representation, we use some classification models to classify input sentences. Those models will be described in detail in the section SECREF13. The first model is TextCNN (figure FIGREF2) proposed in BIBREF11. It only contains CNN blocks following by some Dense layers. The output of multiple CNN blocks with different kernel sizes is connected to each other.

The second model is VDCNN (figure FIGREF5) inspired by the research in BIBREF12. Like the TextCNN model, it contains multiple CNN blocks. The addition in this model is its residual connection.

The third model is a simple LSTM bidirectional model (figure FIGREF15). It contains multiple LSTM bidirectional blocks stacked to each other.

The fourth model is LSTMCNN (figure FIGREF24). Before going through CNN blocks, series of word embedding will be transformed by LSTM bidirectional block. The final model is the system named SARNN (figure FIGREF25). It adds an attention block between LTSM blocks. In this system, we use the Stacking method. In this method, the output of each model is not only class id but also the probability of each class in the set of three classes. This probability will become a feature for the ensemble model. The stacking ensemble model here is a simple full-connection model with input is all of probability that output from sub-model. The output is the probability of each class. 
 Question: What classifier do they use?

Stacking method LSTMCNN SARNN simple LSTM bidirectional model TextCNN

