instruction:
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.
question:
In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel).
Question: Is the performance compared against a baseline model?
answer:
Yes

question:
For annotation purposes, we created snippets of conversations as the ones shown in Example 1 and Example 2 consisting of the parent of the suspected trolling event, the suspected trolling event comment, and all of the direct responses to the suspected trolling event. However, the trade off is that snippets like this allow us to make use of Amazon Mechanical Turk (AMT) to have the dataset annotated, because it is not a big burden for a "turker" to work on an individual snippet in exchange for a small pay, and expedites the annotation process by distributing it over dozens of people. Specifically, for each snippet, we requested three annotators to label the four aspects previously described.
Question: how was annotation done?
answer:
Annotation was done with the help of annotators from Amazon Mechanical Turk on snippets of conversations

question:
Although the competition proposes two different scenarios, in fact, both are guided by the snomed ct ontology for subtask 1, entities must be identified with offsets and mapped to a predefined set of four classes (PROTEINAS, NORMALIZABLES, NO_NORMALIZABLES and UNCLEAR); for subtask 2, a list of all snomed ct ids (sctid) for entities occurring in the text must be given, which has been called concept indexing by the shared task organizers.
Question: What are the two PharmaCoNER subtasks?
answer:
Entity identification with offset mapping and concept indexing