DataHammer/scimrc huggingface dataset:
Question: How did they get relations between mentions?
Evidence: To each node $v_i$ , we associate a continuous annotation $\mathbf {x}_i \in \mathbb {R}^D$ which represents an entity in the context where it was mentioned (details in Section "Node annotations" ). We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges—these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges). Note that MATCH edges when connecting mentions in the same document are mostly included in the set of edges predicted by the coreference system. Having the two types of edges lets us distinguish between less reliable edges provided by the coreference system and more reliable (but also more sparse) edges given by the exact-match heuristic. We treat these three types of connections as three different types of relations. See Figure 2 for an illustration. In addition to that, and to prevent having disconnected graphs, we add a fourth type of relation (COMPLEMENT edge) between any two nodes that are not connected with any of the other relations. We can think of these edges as those in the complement set of the entity graph with respect to a fully connected graph.
We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges—these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges). 
Answer: Assign a value to the relation based on whether mentions occur in the same document, if mentions are identical, or if mentions are in the same coreference chain.
Question: How is gang membership verified?
Evidence: 3. Manual verification of Twitter profiles: We verified each profile discovered manually by examining the profile picture, profile background image, recent tweets, and recent pictures posted by a user. During these checks, we searched for terms, activities, and symbols that we believed could be associated with a gang. For example, profiles whose image or background included guns in a threatening way, stacks of money, showing gang hand signs and gestures, and humans holding or posing with a gun, appeared likely to be from a gang member. Such images were often identified in profiles of users who submitted tweets that contain messages of support or sadness for prisoners or recently fallen gang members, or used a high volume of threatening and intimidating slang language. Only profiles where the images, words, and tweets all suggested gang affiliation were labeled as gang affiliates and added to our dataset. Although this manual verification does have a degree of subjectivity, in practice, the images and words used by gang members on social media are so pronounced that we believe any reasonable analyst would agree that they are gang members. We found that not all the profiles collected belonged to gang members; we observed relatives and followers of gang members posting the same hashtags as in Step 1 to convey similar feelings in their profile descriptions.
Manual verification of Twitter profiles: We verified each profile discovered manually by examining the profile picture, profile background image, recent tweets, and recent pictures posted by a user.
Answer: The answers are shown as follows:
* Manual verification
Question: Is it the first large-scale dataset for QA over social media data proposed by the author?
Evidence: With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge. While previous datasets haveconcentrated on question answering (QA) forformal text like news and Wikipedia, wepresent the first large-scale dataset for QA oversocial media data. To ensure that the tweetswe collected are useful, we only gather tweetsused by journalists to write news articles. Wethen ask human annotators to write questionsand answers upon these tweets. Unlike otherQA datasets like SQuAD in which the answersare extractive, we allow the answers to be ab-stractive. We show that two recently proposedneural models that perform well on formaltexts are limited in their performance when ap-plied to our dataset. In addition, even the fine-tuned BERT model is still lagging behind hu-man performance with a large margin. Our re-sults thus point to the need of improved QAsystems targeting social media text.
While previous datasets have concentrated on question answering (QA) for formal text like news and Wikipedia, we present the first large-scale dataset for QA over social media data.
Answer: Yes.
Question: How does the writer deal with the issue of news suggestions?
Evidence: We approach the news suggestion problem by decomposing it into two tasks:
AEP: Article–Entity placement
ASP: Article–Section placement
In this first step, for a given entity-news pair INLINEFORM0 , we determine whether the given news article INLINEFORM1 should be suggested (we will refer to this as `relevant') to entity INLINEFORM2 . To generate such INLINEFORM3 pairs, we perform the entity linking process, INLINEFORM4 , for INLINEFORM5 .
The article–entity placement task (described in detail in Section SECREF16 ) for a pair INLINEFORM0 outputs a binary label (either `non-relevant' or `relevant') and is formalized in Equation EQREF14 . DISPLAYFORM0 
In the second step, we take into account all `relevant' pairs INLINEFORM0 and find the correct section for article INLINEFORM1 in entity INLINEFORM2 , respectively its profile INLINEFORM3 (see Section SECREF30 ). The article–section placement task, determines the correct section for the triple INLINEFORM4 , and is formalized in Equation EQREF15 . DISPLAYFORM0 
In the subsequent sections we describe in details how we approach the two tasks for suggesting news articles to entity pages.
We approach the news suggestion problem by decomposing it into two tasks: 1. AEP: Article–Entity placement 2. ASP: Article–Section placement
Answer: 1. AEP: Article–Entity placement 2. ASP: Article–Section placement
Question: What approach does the author use to generate samples for the hidden variables?
Evidence: The inference problem consists of predicting the role labels and CLVs (the hidden variables) given the predicate, its voice, and syntactic features of all the identified arguments (the visible variables). We use a collapsed Gibbs-sampling based approach to generate samples for the hidden variables (model parameters are integrated out). The sample counts and the priors are then used to calculate the MAP estimate of the model parameters.
For the monolingual model, the role at a given position is sampled as:
 DISPLAYFORM0 
 where the subscript INLINEFORM0 refers to all the variables except at position INLINEFORM1 , INLINEFORM2 refers to the variables in all the training instances except the current one, and INLINEFORM3 refers to all the model parameters. The above integral has a closed form solution due to Dirichlet-multinomial conjugacy.
For sampling roles in the multilingual model, we also need to consider the probabilities of roles being generated by the CLVs:
 DISPLAYFORM0 
For sampling CLVs, we need to consider three factors: two corresponding to probabilities of generating the aligned roles, and the third one corresponding to selecting the CLV according to CRP.
 DISPLAYFORM0 
 where the aligned roles INLINEFORM0 and INLINEFORM1 are connected to INLINEFORM2 , and INLINEFORM3 refers to all the variables except INLINEFORM4 , INLINEFORM5 , and INLINEFORM6 .
We use the trained parameters to parse the monolingual data using the monolingual model. The crosslingual parameters are ignored even if they were used during training. Thus, the information coming from the CLVs acts as a regularizer for the monolingual models.
We use a collapsed Gibbs-sampling based approach to generate samples for the hidden variables (model parameters are integrated out).
Answer: A collapsed Gibbs-sampling based approach.
Question: Classify each as a chip or a cracker: Goldfish, Doritos, Cheez-It, Cheetos.
Answer: Crackers are Goldfish and Cheez-it, both being baked snacks. Chips are Cheetos and Doritos.
Question: What did the authors use when doing the cross-testing experiments?
Evidence: Question answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language. Three tasks have been proposed for selection-based QA. Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks. Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT Start).
Several datasets have been released for selection-based QA. wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task. feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts. yang:15a introduced WikiQA for answer selection and triggering. jurczyk:16 created SelQA for large real-scale answer triggering. rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension. Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection.
These corpora make it possible to evaluate the robustness of statistical question answering learning. Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made. In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA. We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ).
Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section 4 ).
Answer: A convolutional neural network architecture
Question: Which of these are brands for clothing: Ford, Samsung, H&M, Tommy Hilfiger, Jack & Jones, Kingfisher
Answer: H&M, Tommy Hilfiger, Jack & Jones
Question: Why are cats better than dogs?
Answer: First, let me say that the question represents my own, personal, subjective opinion.  I also adore dogs, just not as much as cats.  I am fully a cat person.
The cat, though.  I am humbled by the task of trying to accurately capture in words the mystery of these beautiful creatures who have chosen to enslave humans.  So instead, because I am also trying to train a model, I will just use a lot of unnecessary adjectives and that will have to do.
Cats have been around humans for a long long time.  I remember as a kid having a book that talked about how in Ancient Egypt cats were revered as holy creatures and kept in luxury in temples of Baast.  The first known evidence of cat domestication is the excavation of a Neolithic grave in Cyprus.  The thing I adore about cats is that they basically haven’t bothered to change themselves since then.  They just carried on being slightly aloof and mysterious, but also affectionate and playful when they feel like it.  And imperious and demanding, of course.  
Cats are incredible communicators.  They have learned to meow at humans because they realised we respond to meows, even though you will very rarely hear a cat meow at another cat (the indignity!).  Meows and chirps are the special condescending language cats developed to communicate with an inferior species.  My own cat can make crystal clear what he wants with looks, body language and the odd meow when he can be bothered.  He is also a total drama queen and obviously manipulative.  If he wants food he will try the ‘I’m hungry’ meow first and if that doesn’t work we get the full on “I’m starving, this is a tragedy, I will die almost immediately if I don’t get food right now”.  I am full of respect for the cats’ ability to manipulate humans and the shamelessness with which they do this.
Cats are also super independent compared to dogs.  No need for walks, no need for the dreaded poo bags, no need for expensive care every time you want to go away for a night.  They love their humans but 100% on their own terms.  
Cats are adorable and cute.  I can’t stress this enough.  Ever seen a cat roll over so you can see its fluffy belly.  How can you not melt.  Ever had a cat make biscuits on your lap (or in the air).  Cuteness overload.  They are even cute when they are mad.  And most cats I’ve met know how to gently tell you what affection is allowed and what isn’t (illegal belly rubs).  They will bat you with a paw, but the claws will be in.  And a cat purring on your lap is the single most relaxing thing in the whole world.
The Swedish word for ‘cat’ is ‘kattepus’.  That in itself seems a really great reason why cats are better than dogs.
The last word goes to Terry Pratchett, author of the Discworld novels, who wrote ‘In ancient times cats were worshipped as gods; they have not forgotten this’.  And neither should we.
Question: What articles of clothing do people typically wear in the winter?
Answer: In the winter, when the weather is typically colder, people generally wear long pants or skirts with nylons, a long sleeve shirt or multiple layers of shirts (like a shirt with a sweater), and a jacket. For footwear, it is common to wear shoes that offer more protection, like boots, and thicker or longer socks. Gloves and hats are also worn in extreme cold weather.
Question: Is Leaning Tower of Pisa structure stabilized?
Evidence: The tower was closed to the public on 7 January 1990, after more than two decades of stabilisation studies and spurred by the abrupt collapse of the Civic Tower of Pavia in 1989.The bells were removed to relieve some weight, and cables were cinched around the third level and anchored several hundred meters away. Apartments and houses in the path of a potential fall of the tower were vacated for safety. The selected method for preventing the collapse of the tower was to slightly reduce its tilt to a safer angle by removing 38 cubic metres (1,342 cubic feet) of soil from underneath the raised end. The tower's tilt was reduced by 45 centimetres (17+1⁄2 inches), returning to its 1838 position. After a decade of corrective reconstruction and stabilization efforts, the tower was reopened to the public on 15 December 2001, and was declared stable for at least another 300 years. In total, 70 metric tons (77 short tons) of soil were removed.
Answer: Leaning Tower of Pisa was closed to public due to an abrupt collapse of Civic tower of Pavia. Public buildings in the path of potential fall of the tower were vacated for safety.The tower was reopened to the public on December 2001, after a decade of corrective reconstruction and stabilization efforts and was declared that it will be stable for at least another 300 years to come
Question: What is meant by the saying “break a leg”?
Answer: “Break a leg” is a way to wish someone good luck, especially before a performance.
Question: What is powerpoint?
Answer: Powerpoint is a popular presentation tool, part of the Microsoft Office suite. People like to use Powerpoint because of its easy-to-use slide layouts and has become the industry standard for individuals, students, and professionals.
Question: Classify each of the Super Bowl winners from the oldest to the most recent (first win): New England Patriots, Philadelphia Eagles, Tampa Bay Buccaneers, St Louis and NY Giants.
Answer: The answer is : 
1. NY Giants in 1991 
2. St Louis in 2000 
3. Tampa Bay in 2003
4. New England 2004 
5. Philadelphia Eagles in 2018
Question: What additional techniques are incorporated?
Evidence: Although the generated code is incoherent and often predict wrong code token, this is expected because of the limited amount of training data. LSTM generally requires a more extensive set of data (100k+ in such scenario) to build a more accurate model. The incoherence can be resolved by incorporating coding syntax tree model in future. For instance–
"define the method tzname with 2 arguments: self and dt."
is translated into–
def __init__ ( self , regex ) :.
The translator is successfully generating the whole codeline automatically but missing the noun part (parameter and function name) part of the syntax.
Although the generated code is incoherent and often predict wrong code token, this is expected because of the limited amount of training data. LSTM generally requires a more extensive set of data (100k+ in such scenario) to build a more accurate model. The incoherence can be resolved by incorporating coding syntax tree model in future. For instance–

"define the method tzname with 2 arguments: self and dt."

is translated into–

def __init__ ( self , regex ) :.

The translator is successfully generating the whole codeline automatically but missing the noun part (parameter and function name) part of the syntax.
Answer: The answers are shown as follows:
* incorporating coding syntax tree model
Question: What are the differences in language use between gang member and the rest of the Twitter population?
Evidence: Figure FIGREF14 summarizes the words seen most often in the gang and non-gang members' tweets as clouds. They show a clear difference in language. For example, we note that gang members more frequently use curse words in comparison to ordinary users. Although cursing is frequent in tweets, they represent just 1.15% of all words used BIBREF21 . In contrast, we found 5.72% of all words posted by gang member accounts to be classified as a curse word, which is nearly five times more than the average curse word usage on Twitter. The clouds also reflect the fact that gang members often talk about drugs and money with terms such as smoke, high, hit, and money, while ordinary users hardly speak about finances and drugs. We also noticed that gang members talk about material things with terms such as got, money, make, real, need whereas ordinary users tend to vocalize their feelings with terms such as new, like, love, know, want, look, make, us. These differences make it clear that the individual words used by gang and non-gang members will be relevant features for gang profile classification.
Although cursing is frequent in tweets, they represent just 1.15% of all words used BIBREF21 . In contrast, we found 5.72% of all words posted by gang member accounts to be classified as a curse word, which is nearly five times more than the average curse word usage on Twitter.
The clouds also reflect the fact that gang members often talk about drugs and money with terms such as smoke, high, hit, and money, while ordinary users hardly speak about finances and drugs. We also noticed that gang members talk about material things with terms such as got, money, make, real, need whereas ordinary users tend to vocalize their feelings with terms such as new, like, love, know, want, look, make, us.
Answer: The answers are shown as follows:
* Although cursing is frequent in tweets, they represent just 1.15% of all words used BIBREF21 . In contrast, we found 5.72% of all words posted by gang member accounts to be classified as a curse word
* gang members talk about material things with terms such as got, money, make, real, need whereas ordinary users tend to vocalize their feelings with terms such as new, like, love, know, want, look, make, us
Question: Why do you get dental erosion?
Answer: Dental erosion is caused by dietary factors , physical factors and indigestion and reflux disease. Dietary acids include carbonated drinks, acidic fruits and juices and sweets. Teeth should not be brushed straight after eating , you should wait 30 minutes after eating before brushing.
Question: What is Angel Falls?
Evidence: Angel Falls (Spanish: Salto Ángel; Pemon language: Kerepakupai Merú meaning "waterfall of the deepest place", or Parakupá Vená, meaning "the fall from the highest point") is a waterfall in Venezuela. It is the world's tallest uninterrupted waterfall, with a height of 979 metres (3,212 ft) and a plunge of 807 m (2,648 ft). The waterfall drops over the edge of the Auyán-tepui mountain in the Canaima National Park (Spanish: Parque Nacional Canaima), a UNESCO World Heritage site in the Gran Sabana region of Bolívar State. The height figure, 979 m (3,212 ft), mostly consists of the main plunge but also includes about 400 metres (1,300 ft) of sloped cascade and rapids below the drop and a 30-metre-high (100 ft) plunge downstream of the talus rapids.
Answer: Angel Falls (Spanish: Salto Ángel; Pemon language: Kerepakupai Merú meaning "waterfall of the deepest place", or Parakupá Vená, meaning "the fall from the highest point") is a waterfall in Venezuela. It is the world's tallest uninterrupted waterfall, with a height of 979 metres (3,212 ft) and a plunge of 807 m (2,648 ft). The waterfall drops over the edge of the Auyán-tepui mountain in the Canaima National Park (Spanish: Parque Nacional Canaima), a UNESCO World Heritage site in the Gran Sabana region of Bolívar State. The height figure, 979 m (3,212 ft), mostly consists of the main plunge but also includes about 400 metres (1,300 ft) of sloped cascade and rapids below the drop and a 30-metre-high (100 ft) plunge downstream of the talus rapids.

The falls are along a fork of the Río Kerepacupai Merú which flows into the Churún River, a tributary of the Carrao River, itself a tributary of the Orinoco River.
Question: Where is Seinäjoki City Hall?
Evidence: The Seinäjoki City Hall is the main municipal administrative building in the city of Seinäjoki, Finland. It is notable for having been designed by the renowned Finnish architect Alvar Aalto.
Answer: Seinäjoki City Hall, located in Seinäjoki, Finland is the city's main municipal administrative building. The building was designed by the renowned Finnish architect Alvar Aalto.
Question: Is Mining Engineering a good degree?
Answer: A Mining Engineering degree is a very good general engineering degree that will expose you to all areas of engineering and science and allow you to apply this knowledge to a specific industry.  All areas of engineering and science include such things as:

Chemical Engineering for mineral extracting and refining.
Civil Engineering for Structures.
Mechanical Engineering for CAD, thermodynamics, and mine design
Process Engineering for all aspects of the mining operation
Electrical Engineering for circuit design and electrical machines.
Geological Engineering for understanding the earth and soil mechanics
Environmental Engineering because we want to extract minerals safely and cleanly.  all 
Computer Engineering to support all of the above   

Despite all the benefits of the above, many mines are located in remote locations and can also be dirty.  This may the viewed as a positive and an adventure, but to others looking for a more urban lifestyle, mining engineering may not be for them.  Even with this, the broad engineering skills you will learn in your mining engineering degree will be applicable to many roles and industries well beyond the mining industry.