Definition: 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.

Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Google search engine. Queries that are likely to be yes/no questions are heuristically identified: we found selecting queries where the first word is in a manually constructed set of indicator words and are of sufficient length, to be effective.

Questions are only kept if a Wikipedia page is returned as one of the first five results, in which case the question and Wikipedia page are given to a human annotator for further processing.

Annotators label question/article pairs in a three-step process. First, they decide if the question is good, meaning it is comprehensible, unambiguous, and requesting factual information. This judgment is made before the annotator sees the Wikipedia page. Next, for good questions, annotators find a passage within the document that contains enough information to answer the question. Annotators can mark questions as “not answerable" if the Wikipedia article does not contain the requested information. Finally, annotators mark whether the question's answer is “yes" or “no". Annotating data in this manner is quite expensive since annotators need to search entire Wikipedia documents for relevant evidence and read the text carefully.
Output: how was the dataset built?

Positive Example 2 -
Input: Using this annotation model, we create a new large publicly available dataset of English tweets.
Output: In what language are the tweets?

Negative Example 1 -
Input: Using this annotation model, we create a new large publicly available dataset of English tweets.
Output: In what language is tweets?

Negative Example 2 -
Input: Questions are gathered from anonymized, aggregated queries to the Google search engine. Queries that are likely to be yes/no questions are heuristically identified: we found selecting queries where the first word is in a manually constructed set of indicator words and are of sufficient length, to be effective.

Questions are only kept if a Wikipedia page is returned as one of the first five results, in which case the question and Wikipedia page are given to a human annotator for further processing.

Annotators label question/article pairs in a three-step process. First, they decide if the question is good, meaning it is comprehensible, unambiguous, and requesting factual information. This judgment is made before the annotator sees the Wikipedia page. Next, for good questions, annotators find a passage within the document that contains enough information to answer the question. Annotators can mark questions as “not answerable" if the Wikipedia article does not contain the requested information. Finally, annotators mark whether the question's answer is “yes" or “no". Annotating data in this manner is quite expensive since annotators need to search entire Wikipedia documents for relevant evidence and read the text carefully.
Output: What is the size of the dataset?

Now complete the following example -
Input: We created eight different classifiers, each of which used one of the following eight features available from a tweet as retrieved from a stream of the Twitter API:

User location (uloc): This is the location the user specifies in their profile. While this feature might seem a priori useful, it is somewhat limited as this is a free text field that users can leave empty, input a location name that is ambiguous or has typos, or a string that does not match with any specific locations (e.g., “at home”). Looking at users' self-reported locations, Hecht et al. BIBREF49 found that 66% report information that can be translated, accurately or inaccurately, to a geographic location, with the other 34% being either empty or not geolocalisable.

User language (ulang): This is the user's self-declared user interface language. The interface language might be indicative of the user's country of origin; however, they might also have set up the interface in a different language, such as English, because it was the default language when they signed up or because the language of their choice is not available.

Timezone (tz): This indicates the time zone that the user has specified in their settings, e.g., “Pacific Time (US & Canada)”. When the user has specified an accurate time zone in their settings, it can be indicative of their country of origin; however, some users may have the default time zone in their settings, or they may use an equivalent time zone belonging to a different location (e.g., “Europe/London” for a user in Portugal). Also, Twitter's list of time zones does not include all countries.

Tweet language (tlang): The language in which a tweet is believed to be written is automatically detected by Twitter. It has been found to be accurate for major languages, but it leaves much to be desired for less widely used languages. Twitter's language identifier has also been found to struggle with multilingual tweets, where parts of a tweet are written in different languages BIBREF50 .

Offset (offset): This is the offset, with respect to UTC/GMT, that the user has specified in their settings. It is similar to the time zone, albeit more limited as it is shared with a number of countries.

User name (name): This is the name that the user specifies in their settings, which can be their real name, or an alternative name they choose to use. The name of a user can reveal, in some cases, their country of origin.

User description (description): This is a free text where a user can describe themselves, their interests, etc.

Tweet content (content): The text that forms the actual content of the tweet. The use of content has a number of caveats. One is that content might change over time, and therefore new tweets might discuss new topics that the classifiers have not seen before. Another caveat is that the content of the tweet might not be location-specific; in a previous study, Rakesh et al. BIBREF51 found that the content of only 289 out of 10,000 tweets was location-specific.
Output: 



Output: What are the eight features mentioned?