Teacher: In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
Teacher: Now, understand the problem? If you are still confused, see the following example:
We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties.
Question: How did they obtain the dataset?
Solution: Extractive
Reason: The answer to this question has been explicitly mentioned in the context, so the question is extractive.

Now, solve this instance: In this section we describe a number of experiments targeted to compare the performance of popular named entity recognition algorithms on our data. We trained and evaluated Stanford NER, spaCy 2.0, and a recurrent model similar to BIBREF13 , BIBREF14 that uses bidirectional LSTM cells for character-based feature extraction and CRF, described in Guillaume Genthial's Sequence Tagging with Tensorflow blog post BIBREF15 . Stanford NER is conditional random fields (CRF) classifier based on lexical and contextual features such as the current word, character-level n-grams of up to length 6 at its beginning and the end, previous and next words, word shape and sequence features BIBREF16 . spaCy 2.0 uses a CNN-based transition system for named entity recognition. The main model that we focused on was the recurrent model with a CRF top layer, and the above-mentioned methods served mostly as baselines.
Question: what ner models were evaluated?
Student:
Extractive