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: It is a well known fact that NLP applications in industrial settings often have to deal with the noisy data. There are different kinds of possible noise namely non-canonical text such as spelling mistakes, typographic errors, colloquialisms, abbreviations, slang, internet jargon, emojis, embedded metadata (such as hashtags, URLs, mentions), non standard syntactic constructions and spelling variations, grammatically incorrect text, mixture of two or more languages to name a few. Such noisy data is a hallmark of user generated text content and commonly found on social media, chats, online reviews, web forums to name a few. 
 Question: What kind is noise is present in typical industrial data?

Answer:  non-canonical text such as spelling mistakes, typographic errors, colloquialisms, abbreviations, slang, internet jargon, emojis, embedded metadata (such as hashtags, URLs, mentions), non standard syntactic constructions and spelling variations, grammatically incorrect text, mixture of two or more languages


Question: A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks. 
 Question: On what model architectures are previous co-occurence networks based?

Answer: in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window connects only adjacent words in the so called word adjacency networks


Question: We recruited 176 AMT workers to participate in our conceptualization task. 
 Question: What crowdsourcing platform was used?

Answer:
AMT


