DataHammer/scimrc huggingface dataset:
Question: Does computational text analysis represent a step towards better understanding social and cultural phenomena?
Evidence: In this phase, we use our models to explore or answer our research questions. For example, given a topic model we can look at the connection between topics and metadata elements. Tags such as “hate speech" or metadata information imply a certain way of organizing the collection. Computational models provide another organization, which may differ in ways that provide more insight into how these categories manifest themselves, or fail to do so.
Moreover, when using a supervised approach, the “errors”, i.e. disagreement between the system output and human-provided labels, can point towards interesting cases for closer analysis and help us reflect on our conceptualizations. In the words of BIBREF2 , they can be “opportunities for interpretation”. Other types of “failures” can be insightful as well. Sometimes there is a “dog that didn't bark” BIBREF49 –i.e., something that everyone thinks we should have found, but we did not. Or, sometimes the failures are telling us about the existence of something in the data that nobody noticed, or thought important, until then (e.g., the large number of travel journals in Darwin's reading lists).
Computational text analysis is not a replacement for but rather an addition to the approaches one can take to analyze social and cultural phenomena using textual data. By moving back and forth between large-scale computational analyses and small-scale qualitative analyses, we can combine their strengths so that we can identify large-scale and long-term trends, but also tell individual stories. For example, the Reddit study on hate speech BIBREF0 raised various follow-up questions: Can we distinguish hate speech from people talking about hate speech? Did people find new ways to express hate speech? If so, did the total amount of online hate speech decrease after all? As possible next steps, a qualitative discourse analyst might examine a smaller corpus to investigate whether commenters were indeed expressing hate speech in new ways; a specialist in interview methodologies might reach out to commenters to better understand the role of online hate speech in their lives. Computational text analysis represents a step towards better understanding social and cultural phenomena, and it is in many cases better suited towards opening questions rather than closing them.
Computational text analysis represents a step towards better understanding social and cultural phenomena, and it is in many cases better suited towards opening questions rather than closing them.
Answer: Yes.
Question: Is society ready for flying cars ?
Answer: No, significant work needs to done in terms of : 
Infrastructure to support cars that can traverse the airspace 
New FAA regulations to manage airspace safety 
New driver safety training and licenses 
New charging stations 
New insurance policies
Question: What are the solar system planets?
Answer: Solar system planets:

- Mercury
- Venus
- Earth
- Mars
- Jupiter
- Saturn
- Uranus
- Neptune
Question: Which of the following famous basketball players have won an NBA championship: Lebron James, Steve Nash, Michael Jordan, Charles Barkley, Dwight Howard, Larry Bird, and Chris Paul
Answer: Chris Paul, Charles Barkley, Dwight Howard, and Steve Nash have never won an NBA championship. Lebron James, Michael Jordan, and Larry Bird have all won an NBA championship. In fact, all three of those players have won multiple championships.
Question: give me a bulleted list of ways to get to the other side of a lake.
Answer: - walk across the suspension bridge hovering over the lake 
- swim across the lake
- if you are a scorpion, hitch a ride on a frogs back and behave
- canoe across the lake
- drive around the lake and reach the other side
- zip line across the lake
- skate across the lake in the winter when the lake is frozen
- water ski across the lake
- lay on a float and hope you end up on the other side of the lake
- drain the lake and walk across on the lake bed
- take a ferry across the lake
- scuba dive across the lake
Question: How object-oriented programming is different from functional programming?
Answer: Functional programming focuses on functions, whereas object-oriented programming focuses on classes and respective objects.
Question: What are the baseline models?
Evidence: We re-implemented five keyphrase extraction models : the first two are commonly used as baselines, the third is a resource-lean unsupervised graph-based ranking approach, and the last two were among the top performing systems in the SemEval-2010 keyphrase extraction task BIBREF0 . We note that two of the systems are supervised and rely on the training set to build their classification models. Document frequency counts are also computed on the training set. Stemming is applied to allow more robust matching. The different keyphrase extraction models are briefly described below:
Each model uses a distinct keyphrase candidate selection method that provides a trade-off between the highest attainable recall and the size of set of candidates. Table summarizes these numbers for each model. Syntax-based selection heuristics, as used by TopicRank and WINGNUS, are better suited to prune candidates that are unlikely to be keyphrases. As for KP-miner, removing infrequent candidates may seem rather blunt, but it turns out to be a simple yet effective pruning method when dealing with long documents. For details on how candidate selection methods affect keyphrase extraction, please refer to BIBREF16 .
Apart from TopicRank that groups similar candidates into topics, the other models do not have any redundancy control mechanism. Yet, recent work has shown that up to 12% of the overall error made by state-of-the-art keyphrase extraction systems were due to redundancy BIBREF6 , BIBREF17 . Therefore as a post-ranking step, we remove redundant keyphrases from the ranked lists generated by all models. A keyphrase is considered redundant if it is included in another keyphrase that is ranked higher in the list.
We re-implemented five keyphrase extraction models : the first two are commonly used as baselines, the third is a resource-lean unsupervised graph-based ranking approach, and the last two were among the top performing systems in the SemEval-2010 keyphrase extraction task (Kim et al., 2010).
Answer: There are no baseline models but five models to be studied.
Question: Think of a number of different ways you can cook eggs
Answer: Eggs are versatile and can be cooked using a number of methods including boiling, scrambling, poaching, frying and baking. They can also be beaten and augmented with other ingredients to make an Omelette or Frittata
Question: What machine learning algorithms are used?
Evidence: Baselines. We compare our approach with several methods BIBREF1 , BIBREF31 , BIBREF11 , BIBREF8 , BIBREF10 , BIBREF39 in two cross-domain settings. Using string kernels, Giménez-Pérez et al. BIBREF10 reported better performance than SST BIBREF31 and KE-Meta BIBREF11 in the multi-source domain setting. In addition, we compare our approach with SFA BIBREF1 , CORAL BIBREF8 and TR-TrAdaBoost BIBREF39 in the single-source setting.
Transductive string kernels. We present a simple and straightforward approach to produce a transductive similarity measure suitable for strings. We take the following steps to derive transductive string kernels. For a given kernel (similarity) function INLINEFORM0 , we first build the full kernel matrix INLINEFORM1 , by including the pairwise similarities of samples from both the train and the test sets. For a training set INLINEFORM2 of INLINEFORM3 samples and a test set INLINEFORM4 of INLINEFORM5 samples, such that INLINEFORM6 , each component in the full kernel matrix is defined as follows: DISPLAYFORM0
We next present a simple yet effective approach for adapting a one-versus-all kernel classifier trained on a source domain to a different target domain. Our transductive kernel classifier (TKC) approach is composed of two learning iterations. Our entire framework is formally described in Algorithm SECREF3 .
We compare our approach with several methods BIBREF1 , BIBREF31 , BIBREF11 , BIBREF8 , BIBREF10 , BIBREF39 in two cross-domain settings. Using string kernels, Giménez-Pérez et al. BIBREF10 reported better performance than SST BIBREF31 and KE-Meta BIBREF11 in the multi-source domain setting. In addition, we compare our approach with SFA BIBREF1 , CORAL BIBREF8 and TR-TrAdaBoost BIBREF39 in the single-source setting.
Transductive string kernels. We present a simple and straightforward approach to produce a transductive similarity measure suitable for strings.
Our transductive kernel classifier (TKC) approach is composed of two learning iterations. 
Answer: The answers are shown as follows:
* string kernels
* SST
* KE-Meta
* SFA
* CORAL
* TR-TrAdaBoost
* Transductive string kernels
* transductive kernel classifier
Question: What is the best place for tacos in San Francisco?
Answer: This answer depends on whether you want to optimize for ambiance or the quality of the food. If the latter, the Mission District of San Francisco has countless taquerias and taco trucks with tasty and authentic street tacos. If you are looking for a night out with friends or a date night, I would suggest going to a Mexican restaurant - and my favorite is Tacko in the Marina neighborhood of San Francisco. Tacko has amazing guacamole and cheap pitchers of margaritas.
Question: Name a few popular racquet sports
Answer: Tennis, Ping Pong, Pickleball, Squash, Badminton
Question: Why do people like playing games?
Answer: There are many reasons why people like playing games. 1. Games are different from real world and hence players can usually do/achieve a lot more than what they can do in the real world. 2. Games give quick results 3. People/players can feel less responsibility (e.g. players can just quit the games if the games do not go well) 

However, because of these very reasons, games also have side-effects such as making people be less responsible, want more quick responsive answers, and not be able to discern between real world and game world, etc.
Question: How many seasons of Survivor has there been?
Evidence: The United States version is produced by Mark Burnett and hosted by Jeff Probst, who also serves as an executive producer. Each competition is called a season, has a unique name, and lasts from 13 to 16 episodes. The first season, Survivor: Borneo, was broadcast as a summer replacement show in 2000. Starting with the third season, Survivor: Africa, there have been two seasons aired during each U.S. television season. Starting with the forty-first season, no subtitle has been used in promotion of the season. Instead, the show began following a number format similar to Big Brother and The Amazing Race.

In the first season, there was a 75-person crew. By season 22, the crew had grown to 325 people.

A total of 662 contestants have competed on Survivor's 44 seasons.
Answer: There have been 44 seasons of Survivor.
Question: Write a poem about lost friendship
Answer: Thought we were sandstorms,
Getting rid of everything on our way.
Thought we were revolutionaries,
Changing the usual ways of society.
Thought we were inseparable,
Holding our hands firmly.
Thought we were makers of our future,
Closing our eyes at future.
Thought of the new ideas we would implement,
Being ignorant of life's ways.
But, now I know,
We were never destined to take the same path,
Never will we sit and chat as we once used to.
Question: what are the best tips for parenting?
Answer: Be role models to your children. Behave the way you want your kids to behave. Spend plenty of time with them as they grow up. Be a patient, non-judgemental, supportive companion when they needed. Allow them to pursue their own dreams.
Question: Do the authors mention any confounds to their study?
Evidence: There are several caveats in our work: first, tweet sentiment is rarely binary (this work could be extended to a multinomial or continuous model). Second, our results are constrained to Twitter users, who are known to be more negative than the general U.S. population BIBREF9 . Third, we do not take into account the aggregate effects of continued natural disasters over time. Going forward, there is clear demand in discovering whether social networks can indicate environmental metrics in a “nowcasting" fashion. As climate change becomes more extreme, it remains to be seen what degree of predictive power exists in our current model regarding climate change sentiments with regards to natural disasters.
There are several caveats in our work: first, tweet sentiment is rarely binary (this work could be extended to a multinomial or continuous model). Second, our results are constrained to Twitter users, who are known to be more negative than the general U.S. population BIBREF9 . Third, we do not take into account the aggregate effects of continued natural disasters over time. Going forward, there is clear demand in discovering whether social networks can indicate environmental metrics in a “nowcasting" fashion. As climate change becomes more extreme, it remains to be seen what degree of predictive power exists in our current model regarding climate change sentiments with regards to natural disasters.
Answer: No.
Question: Which one of these is a music band, and which one is a Football team : Manchester United, Linkin Park ?
Answer: Linkin Park is a music band. Manchester United is a Football team.
Question: What is the source of the dataset?
Evidence: We work with a dataset composed of 3,206 news articles, each labeled real or fake, with a perfect 50/50 split between 1,603 real and fake articles, respectively. Fake articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera.
We work with a dataset composed of 3,206 news articles, each labeled real or fake, with a perfect 50/50 split between 1,603 real and fake articles, respectively. Fake articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera.
Answer: Online sites tagged as fake news site by Verafiles and NUJP and news website in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera
Question: What are common titles for the executives of large companies?
Answer: Common titles for executives of large companies include:
- Chief Executive Officer (CEO)
- Chief Technology Officer (CTO)
- Chief Financial Officer (CFO)
- Chief Legal Officer (CLO)
- Chief Product Officer (CPO)
Question: What are five totally distinct, creative ways to describe the usefulness of a broken mind.
Answer: 1. A broken mind is like a rusty hinge, capable of opening up new possibilities.
2. A broken mind is like a cracked mirror, reflecting a distorted view of reality.
3. A broken mind is like a sieve, allowing ideas to pass through and filter out the important ones.
4. A broken mind is like a pebble in a stream, providing a gentle nudge to go in a new direction.
5. A broken mind is like a silent voice, conveying a message that is not always heard.
Question: What is the genomics of disease?
Answer: With the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases including infertility, breast cancer or Alzheimer's disease. Genome-wide association studies are a useful approach to pinpoint the mutations responsible for such complex diseases. Through these studies, thousands of DNA variants have been identified that are associated with similar diseases and traits. Furthermore, the possibility for genes to be used at prognosis, diagnosis or treatment is one of the most essential applications. Many studies are discussing both the promising ways to choose the genes to be used and the problems and pitfalls of using genes to predict disease presence or prognosis.

Genome-wide association studies have successfully identified thousands of common genetic variants for complex diseases and traits; however, these common variants only explain a small fraction of heritability. Rare variants may account for some of the missing heritability. Large-scale whole genome sequencing studies have rapidly sequenced millions of whole genomes, and such studies have identified hundreds of millions of rare variants. Functional annotations predict the effect or function of a genetic variant and help to prioritize rare functional variants, and incorporating these annotations can effectively boost the power of genetic association of rare variants analysis of whole genome sequencing studies. Some tools have been developed to provide all-in-one rare variant association analysis for whole-genome sequencing data, including integration of genotype data and their functional annotations, association analysis, result summary and visualization. Meta-analysis of whole genome sequencing studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes.
Question: What is family time?
Answer: Family during is time where each person in the family is actively engaged in the same activity together.  Family activities like watching a movie together, eating a meal together, going to the circus together, exercising together, or going for a walk is all good family time.  During family time you can share what has been going on in your own life and learn about the other members of your family.  Family time is important to strengthen the bonds you all have with one another.
Question: My coffee tastes too acidic, what can I do to balance it out?
Answer: Here are some of the steps you can take to tweak your coffee brewing process:

* you might be under extracting, try grinding you coffee beans finer
* another way to increase your extraction rate is to use less coffee grinds or more water
* if you’re doing the pour-over method, blooming the coffee first might help removing the co2, improving the extraction 
* you might want to try a darker roast, these have more nutty and chocolaty flavors that might better fit your taste
Question: Given a reference text that describes about hotpot, from which country does this dish originate?
Evidence: Hot pot or hotpot (simplified Chinese: 火锅; traditional Chinese: 火鍋; pinyin: huǒguō; lit. 'fire pot'), also known as soup-food or steamboat, is a dish whereby a heat source placed on the dining table keeps a pot of soup stock simmering, and a variety of Chinese foodstuffs and ingredients are served beside the pot for the diners to put into the hot stock.
Answer: Hot pot or Hotpot is originated from China.
Question: Extract the ways to spell "Muscovy duck" using the sample as a reference, and place them in a list.
Evidence: The Muscovy duck has been domesticated for centuries, and is widely traded as "Barbary duck". Muscovy breeds are popular because they have stronger-tasting meat — sometimes compared to roasted beef — than that of the domestic duck. The meat is lean when compared to the fatty meat of mallard-derived ducks, its leanness and tenderness being often compared to veal. Muscovy ducks are also less noisy, and sometimes marketed as a "quackless" duck; even though they are not completely silent, they do not actually quack (except in cases of extreme stress). The carcass of a Muscovy duck is also much heavier than most other domestic ducks, which makes it ideal for the dinner table.
Answer: - Muscovy duck
- Barbary duck
- Quackless duck
Question: I'm considering buying a pocket knife for my child. How do I know which one I should buy?
Answer: On the market there are many different versions of pocket knives for different purposes and qualities. There are even pocket knives designed specifically for younger children who have no or very little experience with pocket knives, where the blades are not as sharp as usual. Color, size, functions and cost are other factors to consider.
Question: What are the reasons why the authors use both migration models?
Evidence: Simply using NMT systems trained on out-of-domain data for in-domain translation is known to perform badly. In order to effectively use large-scale out-of-domain data for our extremely low-resource task, we propose to perform domain adaptation through either (a) conventional fine-tuning, where an NMT system trained on out-of-domain data is fine-tuned only on in-domain data, or (b) mixed fine-tuning BIBREF9 , where pre-trained out-of-domain NMT system is fine-tuned using a mixture of in-domain and out-of-domain data. The same options are available for transferring from Ja INLINEFORM0 En and Ru INLINEFORM1 En to Ja INLINEFORM2 Ru.
We inevitably involve two types of transfer learning, i.e., domain adaptation for Ja INLINEFORM0 En and Ru INLINEFORM1 En and multilingual transfer for Ja INLINEFORM2 Ru pair. Among several conceivable options for managing these two problems, we examined the following multistage fine-tuning.
Pre-train a multilingual model only on the Ja INLINEFORM0 En and Ru INLINEFORM1 En out-of-domain parallel data (I), where the vocabulary of the model is determined on the basis of the in-domain parallel data in the same manner as the M2M NMT models examined in Section SECREF4 .
Fine-tune the pre-trained model (I) on the in-domain Ja INLINEFORM0 En and Ru INLINEFORM1 En parallel data (fine-tuning, II) or on the mixture of in-domain and out-of-domain Ja INLINEFORM2 En and Ru INLINEFORM3 En parallel data (mixed fine-tuning, III).
Further fine-tune the models (each of II and III) for Ja INLINEFORM0 Ru on in-domain parallel data for this language pair only (fine-tuning, IV and VI) or on all the in-domain parallel data (mixed fine-tuning, V and VII).
We chose this way due to the following two reasons. First, we need to take a balance between several different parallel corpora sizes. The other reason is division of labor; we assume that solving each sub-problem one by one should enable gradual shift of parameters.
We chose this way due to the following two reasons. First, we need to take a balance between several different parallel corpora sizes. The other reason is division of labor; we assume that solving each sub-problem one by one should enable gradual shift of parameters.
Answer: First, they need to take a balance between several different parallel corpora sizes. The other reason is division of labor; they assume that solving each sub-problem one by one should enable gradual shift of parameters.
Question: What dataset do the authors describe?
Evidence: How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as "polite refusal" score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user's perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.
We describe a dataset developed for Named Entity Recognition in German federal court decisions.
Answer: It is a dataset developed for Named Entity Recognition in German federal court decisions.
Question: how to use coffee mug other than drinking
Answer: Use mug as a golf put
Use mug to grow a plant
Use mug for shaving vessel
Use mug as pencil holder
Use mug as a mouse trap
Question: What is the difference between Columbia and Colombia.
Answer: Colombia is a country located in South America.  Columbia can refer to a university or a clothing company.  Columbia University is an Ivy League University in New York City.  Columbia Sportsware is an American clothing company.