In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: Level B categorizes the type of offense and two labels are used: targeted (TIN) and untargeted (INT) insults and threats.

Targeted Insult (TIN): Posts which contain an insult/threat to an individual, group, or others (see next layer);

Untargeted (UNT): Posts containing non-targeted profanity and swearing. Posts with general profanity are not targeted, but they contain non-acceptable language. Level C categorizes the targets of insults and threats as individual (IND), group (GRP), and other (OTH).

Individual (IND): Posts targeting an individual. It can be a a famous person, a named individual or an unnamed participant in the conversation. Insults and threats targeted at individuals are often defined as cyberbulling.

Group (GRP): The target of these offensive posts is a group of people considered as a unity due to the same ethnicity, gender or sexual orientation, political affiliation, religious belief, or other common characteristic. Many of the insults and threats targeted at a group correspond to what is commonly understood as hate speech.

Other (OTH): The target of these offensive posts does not belong to any of the previous two categories (e.g. an organization, a situation, an event, or an issue).
[A]: What kinds of offensive content are explored?


[Q]:  Note that we adopt a parsimonious design principle in our modelling: both Centroid and Naïve Bayes are parameter-free models, $k$NN only depends on the choice of $k$, and KDE uses a single bandwidth parameter $h$. A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule; A Naïve Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class;
[A]: How does the parameter-free model work?


[Q]: The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 . 
[A]:
Where are the hotel reviews from?