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.

Input: Consider Input: The annotation projection method, which we follow in this work, is one way to address this problem. It was introduced for POS tagging, base noun phrase bracketing, NER tagging, and inflectional morphological analysis BIBREF29 but it has also been used for dependency parsing BIBREF30 , role labeling BIBREF31 , BIBREF32 and semantic parsing BIBREF26 .
Question: Do the authors test their annotation projection techniques on tasks other than AMR?

Output: No

Input: Consider Input: 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).
Question: What kinds of offensive content are explored?

Output: non-targeted profanity and swearing, targeted insults such as cyberbullying, offensive content related to ethnicity, gender or sexual orientation, political affiliation, religious belief, and anything belonging to hate speech

Input: Consider Input: Even if the optional lemmatization step is applied, one can still aim at further reducing the graph complexity by merging similar vertices. This step is called meta vertex construction. The meta-vertex construction step works as follows. Let INLINEFORM0 represent the set of vertices, as defined above. A meta vertex INLINEFORM1 is comprised of a set of vertices that are elements of INLINEFORM2 , i.e. INLINEFORM3 . Let INLINEFORM4 denote the INLINEFORM5 -th meta vertex. We construct a given INLINEFORM6 so that for each INLINEFORM7 , INLINEFORM8 's initial edges (prior to merging it into a meta vertex) are rewired to the newly added INLINEFORM9 . Note that such edges connect to vertices which are not a part of INLINEFORM10 . Thus, both the number of vertices, as well as edges get reduced substantially. This feature is implemented via the following procedure: Meta vertex candidate identification. Edit distance and word lengths distance are used to determine whether two words should be merged into a meta vertex (only if length distance threshold is met, the more expensive edit distance is computed). The meta vertex creation. As common identifiers, we use the stemmed version of the original vertices and if there is more than one resulting stem, we select the vertex from the identified candidates that has the highest centrality value in the graph and its stemmed version is introduced as a novel vertex (meta vertex).
Question: How are meta vertices computed?
Output: Meta vertex candidate identification. Edit distance and word lengths distance are used to determine whether two words should be merged into a meta vertex (only if length distance threshold is met, the more expensive edit distance is computed). The meta vertex creation. As common identifiers, we use the stemmed version of the original vertices and if there is more than one resulting stem, we select the vertex from the identified candidates that has the highest centrality value in the graph and its stemmed version is introduced as a novel vertex (meta vertex).