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: We train MaLOPa on the concantenation of training sections of all seven languages. 
 Question: How many languages have this parser been tried on?

Output: seven


Input: Consider Input: To collect a diverse training dataset, we have randomly sampled 1000 posts each from the subreddits politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. 
 Question: what are the topics pulled from Reddit?

Output: politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. 


Input: Consider 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. 
 Question: What are the eight features mentioned?


Output: User location (uloc) User language (ulang) Timezone (tz) Tweet language (tlang) Offset (offset) User name (name) User description (description) Tweet content (content)