TASK DEFINITION: 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.
PROBLEM: We extract user's age by applying regular expression patterns to profile descriptions (such as "17 years old, self-harm, anxiety, depression") BIBREF41 . We compile "age prefixes" and "age suffixes", and use three age-extraction rules: 1. I am X years old 2. Born in X 3. X years old, where X is a "date" or age (e.g., 1994). We selected a subset of 1464 users INLINEFORM0 from INLINEFORM1 who disclose their gender in their profile description. 
 Question: Where does the information on individual-level demographics come from?

SOLUTION: From Twitter profile descriptions of the users.

PROBLEM: There are three main components to the model: 1) input encoder 2) dynamic memory, and 3) output module. We will describe these three modules in details. The input encoder and output module implementations are similar to the Entity Network BIBREF17 and main novelty lies in the dynamic memory. We describe the operations executed by the network for a single example consisting of a document with $T$ sentences, where each sentence consists of a sequence of words represented with $K$ -dimensional word embeddings $\lbrace e_1, \ldots , e_N\rbrace $ , a question on the document represented as another sequence of words and an answer to the question. 
 Question: How is knowledge stored in the memory?

SOLUTION: entity memory and relational memory.

PROBLEM: To test the proposed methods ability to generate unsupervised words, it was necessary to devise a method of measuring word relevance. Topic modeling was used based on the assumption that words found in the same topic are more relevant to one another then words from different topics BIBREF14. The complete 200k headline dataset BIBREF11 was modeled using a Naïve Bayes Algorithm BIBREF15 to create a word-category co-occurrence model. The top 200 most relevant words were then found for each category and used to create the topic table SECREF12. It was assumed that each category represented its own unique topic.

The number of relevant output words as a function of the headline’s category label were measured, and can be found in figure SECREF4. The results demonstrate that the proposed method could correctly identify new words relevant to the input topic at a signal to noise ratio of 4 to 1. 
 Question: How do they determine similarity between predicted word and topics?

SOLUTION:
number of relevant output words as a function of the headline’s category label