DataHammer/scimrc huggingface dataset:
Question: What was the hypothesis for the team's study?
Evidence: We have presented a method for quantifying the similarity and relatedness between two terms that integrates pair–wise similarity scores into second–order vectors. The goal of this approach is two–fold. First, we restrict the context used by the vector measure to words that exist in the biomedical domain, and second, we apply larger weights to those word pairs that are more similar to each other. Our hypothesis was that this combination would reduce the amount of noise in the vectors and therefore increase their correlation with human judgments. We evaluated our method on datasets that have been manually annotated for relatedness and similarity and found evidence to support this hypothesis. In particular we discovered that guiding the creation of a second–order context vector by selecting term pairs from biomedical text based on their semantic similarity led to improved levels of correlation with human judgment.
We also explored using a threshold cutoff to include only those term pairs that obtained a sufficiently large level of similarity. We found that eliminating less similar pairs improved the overall results (to a point). In the future, we plan to explore metrics to automatically determine the threshold cutoff appropriate for a given dataset and measure. We also plan to explore additional features that can be integrated with a second–order vector measure that will reduce the noise but still provide sufficient information to quantify relatedness. We are particularly interested in approaches that learn word, phrase, and sentence embeddings from structured corpora such as literature BIBREF23 and dictionary entries BIBREF24 . Such embeddings could be integrated into a second–order vector or be used on their own.
Finally, we compared our proposed method to other distributional approaches, focusing on those that used word embeddings. Our results showed that integrating semantic similarity measures into second–order co–occurrence vectors obtains the same or higher correlation with human judgments as do various different word embedding approaches. However, a direct comparison was not possible due to variations in the subsets of the UMNSRS evaluation dataset used. In the future, we would not only like to conduct a direct comparison but also explore integrating semantic similarity into various kinds of word embeddings by training on pair–wise values of semantic similarity as well as co–occurrence statistics.
Our hypothesis was that this combination would reduce the amount of noise in the vectors and therefore increase their correlation with human judgments.
Answer: Their hypothesis was that this combination would reduce the amount of noise in the vectors and therefore increase their correlation with human judgments.
Question: Help me come up with 3 ideas for a new business and how I can start it
Answer: There are many businesses you could think about creating. Here are a few examples:
- Services: you can create a firm that provides services such as marketing, technical consulting, or strategic advising. You can start this business by examining your professional expertise, building marketing materials, and networking to find your first customers.
- Product: you can create a product in a domain you're passionate about is a great way of starting a business. This could be small scale boutique items such as candles or soaps made at home and sold at local markets or on a website like Etsy. This could also include something mass manufactured, either contracted out or involving significant capital expenditure in order to start development.
- Training: you can start a training or education program in an industry where you have expertise. There are online platforms for hosting content and managing payments. You can also leverage social media in order to build a funnel of customers.
There are many different potential businesses you might create but services, product, and training businesses are good domains to examine as you look to start your business.
Question: What are the baselines?
Evidence: We use GraphParser without paraphrases as our baseline. This gives an idea about the impact of using paraphrases.
We compare our paraphrasing models with monolingual machine translation based model for paraphrase generation BIBREF24 , BIBREF36 . In particular, we use Moses BIBREF37 to train a monolingual phrase-based MT system on the Paralex corpus. Finally, we use Moses decoder to generate 10-best distinct paraphrases for the test questions.
We use GraphParser without paraphrases as our baseline. This gives an idea about the impact of using paraphrases
We compare our paraphrasing models with monolingual machine translation based model for paraphrase generation BIBREF24 , BIBREF36 . In particular, we use Moses BIBREF37 to train a monolingual phrase-based MT system on the Paralex corpus. Finally, we use Moses decoder to generate 10-best distinct paraphrases for the test questions.
Answer: The answers are shown as follows:
* GraphParser without paraphrases
* monolingual machine translation based model for paraphrase generation
Question: Given the paragraph is Strip the most famous hotel in Las Vegas?
Evidence: The Las Vegas Strip is a stretch of Las Vegas Boulevard South in Clark County, Nevada, that is known for its concentration of resort hotels and casinos. The Strip, as it is known, is about 4.2 mi (6.8 km) long, and is immediately south of the Las Vegas city limits in the unincorporated towns of Paradise and Winchester, but is often referred to simply as "Las Vegas".

Many of the largest hotel, casino, and resort properties in the world are on the Strip, known for its contemporary architecture, lights, and wide variety of attractions. Its hotels, casinos, restaurants, residential high-rises, entertainment offerings, and skyline have established the Strip as one of the most popular and iconic tourist destinations in the world and is one of the driving forces for Las Vegas's economy. Most of the Strip has been designated as an All-American Road, and the North and South Las Vegas Strip routes are classified as Nevada Scenic Byways and National Scenic Byways.
Answer: The Strip is not a hotel in Las Vegas, but rather a 4.2-mile-long boulevard that is home to many of the city's largest hotels, casinos, and resort properties.
Question: How do you make a peanut butter and jelly sandwich?
Answer: You need a few ingredients to make a peanut butter and jelly sandwich. First, you need two slices of bread, which are crucial to the recipe. Next, you will need peanut butter. Some people prefer smooth peanut butter, while others might enjoy crunchy peanut butter. This choice is up to you. The next ingredient is jelly. Like peanut butter, there are many choices of jelly to choose from. The most popular jelly is grape; many people like raspberry or strawberry jelly. Once you have all your ingredients, it is time to assemble your sandwich. First, you lay a piece of bread on your plate. Next, take a knife to spread peanut butter on the slice of bread on the plate. How much peanut butter you spread is up to your preference. Next, take the other slice of bread and spread your jelly on it. Once each piece of bread has either jelly or peanut butter on it, take the two slices of bread and put them together. The final step is to cut your sandwich. Some enjoy eating the sandwich whole, while others enjoy a diagonal cut or a cut down the middle. You have now created your peanut butter and jelly sandwich.
Question: What task does the paper research?
Evidence: Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zero-shot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot setting.
In this paper, we systematically explore zeroshot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus.
Answer: Reading comprehension (RC) tasks.
Question: Name the vocalists of Vengaboys.
Evidence: Vengaboys (/ˈbɛŋ.ɡæ.bɔɪz/) is a Dutch Eurodance music group based in Rotterdam. The group was the creation of Dutch producers Wessel van Diepen and Dennis van den Driesschen (known as Danski and Delmundo). It consists of lead vocalist Kim Sasabone, female vocalist Denise Post-Van Rijswijk and male vocalists Robin Pors and Donny Latupeirissa. Casting and selection of the act was done by van Diepen.
The group enjoyed commercial success in the late 1990s. They are best known for their hit singles "We Like to Party", "Boom, Boom, Boom, Boom!!", and "We're Going to Ibiza", the latter two of which topped the UK Singles Chart. They have sold an estimated 25 million records worldwide. On 2 May 2001, the World Music Awards gave Vengaboys the award for best-selling dance group of the year. In 2019, Dutch online newspaper Nu.nl called Vengaboys the most successful Dutch pop group in history.
Answer: The vocalists of Vengaboys are Kim Sasabone, Denise Post-Van Rijswijk, Robin Pors and Donny Latupeirissa.
Question: Which query explanation method was preffered by the users in terms of correctness?
Evidence: Results in Table TABREF64 show the correctness rates of these scenarios. User correctness score is superior to that of the baseline parser by 7.5% (from 37.1% to 44.6%), while the hybrid approach outscores both with a correctness of 48.7% improving the baseline by 11.6%. For the user and hybrid correctness we used a INLINEFORM0 test to measure significance. Random queries and tables included in the experiment are presented in Table TABREF66 . We also include a comparison of the top ranked query of the baseline parser compared to that of the user.
Results in Table TABREF64 show the correctness rates of these scenarios. User correctness score is superior to that of the baseline parser by 7.5% (from 37.1% to 44.6%), while the hybrid approach outscores both with a correctness of 48.7% improving the baseline by 11.6%.
Answer: The answers are shown as follows:
* hybrid approach
Question: What is an Oxford comma and why do editors have such strong feelings about their use?
Answer: An Oxford comma is a comma before the last item in a series or list. For example take the following two statements:

I was chased by the fox, the bear and the dog.
I was chased by the fox, the bear, and the dog.

The meaning is the same but the second statement contains an Oxford comma.

Some say the Oxford comma adds clarity to some statements. This is apparent when there can be ambiguity between a list and an appositive. Take the following two example statements:

We went to dinner with our coaches, Chris and Terry.
We went to dinner with our coaches, Chris, and Terry.

The first statement can appear to imply that we went to dinner with two people whose names are Chris and Terry who are both our coaches. There is no such implication that can be extracted from the second statement. The second statement implies we went to dinner with at least 4 people: Chris, Terry, and at least two coaches. This type of ambiguity is what proponents of the Oxford comma latch onto in its defense.

On the other hand, opponents of the Oxford comma claim it should be avoided in favor of rewording sentences when necessary. The sentence:

We went to dinner with our coaches, Chris, and Terry.

Can easily be reworded as:

We went to dinner with Chris and Terry, as well as our coaches.

The "as well as" language is claimed to be more clear than the Oxford comma albeit less concise. Editors who oppose the Oxford comma typically privilege clarity over conciseness.
Question: Does XNLG outperform all the baseline models on both French and Chinese AS?
Evidence: We conduct experiments on cross-lingual abstractive summarization (AS). AS is the task of converting the input sentences into summaries while preserving the key meanings. For evaluation, we use English/French/Chinese Gigaword to extract the first sentence and the headline of each article, and regard them as input document and predicted summaries, respectively. For each language, we sample 500k/5k/5k examples for training/validation/test.
In the zero-shot setting, we only use English data for training, and directly evaluate the model on other languages. In Table TABREF22 and Table TABREF23, we present the results for French/Chinese AS, which are evaluated by the ROUGE-1, ROUGE-2 and ROUGE-L metrics. We also report the results of supervised AS in Table TABREF21 for reference. We find that Xnlg outperforms all the baseline models on both French and Chinese AS. Comparing with French, there is a larger gap between baselines and our model on zero-shot Chinese AS, which indicates that the error propagation issue is more serious on distant language pairs.
We also report the results of supervised AS in Table 6 for reference. We find that XNLG outperforms all the baseline models on both French and Chinese AS.
Answer: Yes.
Question: What are some of the best restaurants to try in Las Vegas, Nevada?
Answer: Carbone, Din Tai Fung, Bacchanal Buffet
Question: As an immigrant , do you miss your country?
Answer: As an immigrant, it feels like you are away from your family. It feels like you work hard to give good life to your family. But with all that sacrifice you want to give best of both worlds to your kid. But when you hear news like shootings in school, as an immigrant we get afraid. I wish if there is a world which is so beautiful that people love each other. They care for each other. There are no rules that always keep you at the edge if you have to uproot your family from one country to another because of visa restrictions and what not. As an immigrant we want to raise our child in the best way possible. I am still looking for such a place on earth. If anyone of you are aware of such a place, please let me know., I hope the world in its entiriety becomes a peaceful, happy clean place to live.
Question: What does a healthy morning routine look like?
Answer: Here's an example of a healthy morning routine. You wake up at least an hour or two before your obligations begin. You start your day with a meditation practice so that you can clear your mind and focus on your intentions for the day. Then you spend at least 30 minutes on exercise. After working up a sweat, you take a shower and go through your morning getting-ready routine. You prepare coffee and breakfast, and read or spend time with a loved one as you eat. Then you begin your duties for the day.
Question: Compare and contrast the strategy of Tesla to that of Nissan. Why has Tesla been successful in the electric vehicle market while Nissan has yet to?
Answer: Nissan introduced Leaf in 2011, 18 months before Tesla launched its first car, the Model S. Despite the head start, Nissan Leaf is discontinued as of 2023. Let’s explore why Nissan Leaf failed while Tesla succeeded.
While Nissan Leaf was innovative and introduced an alternative energy car, it was just a better car, but more is needed for consumers to adopt it. With the 9X effect, consumers were skeptical about its performance (range), unable to see the need for an electric car as they were satisfied with the reliable ICE (internal combustion engine) cars. Nissan Leaf is a good car on paper. However, consumers were unwilling to switch because of the status quo bias. Also, Nissan needed to build an ecosystem of products around electric vehicles, like charging stations. It relied on the traditional auto-sales process, which is already cumbersome, and consumers view it as a haggle.

On the other hand, Tesla came with 10X improvements overall in customer experience, drivability, charging stations, integrated hardware and software (over-the-wire updates), and direct to consumer sales model with zero advertising. Tesla broke the status quo bias of the consumers with the 9x effect. Telsa’s messaging is the key that stuck with the customers, planet-friendly cars.
Question: Can the paradigm followed by LD2NL be reused and ported to RDF and OWL?
Evidence: NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons.
Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available.
Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL.
Answer: Yes, they can.
Question: List me some details regarding carreer of lawyer Harry L. Shorstein
Evidence: Harry L. Shorstein (born August 3, 1940) is an American lawyer who served as State Attorney for Florida's Fourth Judicial Circuit Court, covering Duval, Clay and Nassau counties, from 1991–2008. A member of the Democratic Party, he was appointed to the post in 1991 by Governor Lawton Chiles to fill the remaining term of Ed Austin, who resigned to successfully run for mayor of Jacksonville. He was elected to a full term in 1992, re-elected in 1996, and ran unopposed in 2000 and 2004. At a February 6, 2007 news conference, the 66-year-old lawyer announced that he would not run for re-election in 2008. He subsequently returned to private practice, and was succeeded as State Attorney by Angela Corey.
Answer: 1. From 1991 to 2008, Harry L. Shorstein, an American attorney who was born on August 3, 1940, served as State Attorney for Florida's Fourth Judicial Circuit Court, which includes Duval, Clay, and Nassau counties.
2. A Democrat, he was chosen by Governor Lawton Chiles to serve Ed Austin's unexpired term after the latter resigned to run successfully for mayor of Jacksonville.
3. He won re-election to a full term in 1992, was unchallenged in 2000 and 2004 and was re-elected in 1996.
4. The 66-year-old attorney declared he would not seek re-election in 2008 at a news conference on February 6, 2007.
5. He later went back to private practice, and Angela Corey took over as state attorney.
Question: Given these paragraphs about battery storage power stations, for how long battery power storage was cheaper than open cycle gas turbine power as of 2019?
Evidence: As of 2021, the power and capacity of the largest individual battery storage power plants is an order of magnitude less than that of the largest pumped storage power plants, the most common form of grid energy storage. For example, the Bath County Pumped Storage Station, the second largest in the world, can store 24GWh of electricity and dispatch 3GW while the first phase of Vistra Energy's Moss Landing Energy Storage Facility can store 1.2GWh and dispatch 300MW. Grid batteries do not however have to be large, and smaller ones can be deployed widely across a grid for greater redundancy.

As of 2019, battery power storage is cheaper than open cycle gas turbine power for use up to two hours, and there was around 365 GWh of battery storage deployed worldwide, growing extremely rapidly. Levelized cost of electricity from battery storage has fallen rapidly, halving in two years to US$150 per MWh as of 2020.
Answer: As of 2019, battery power storage is cheaper than open cycle gas turbine power for use up to two hours.
Question: What type of YouTube Channel did Danla Bilic have?
Evidence: Aktepe first drew attention to herself by a series of incidents on Twitter. Aktepe, who opened a makeup channel on YouTube, gained a significant number of subscribers over a short period. Subsequently, her style of content diversified to include vlogs, joint broadcasts with singers, models, and other Internet celebrities. She also participated in various television programs. Throughout her career, Aktepe's behavior was occasionally criticized and legal proceedings were initiated by some individuals and institutions against her.
Answer: Danla Bilic started a makeup channel on YouTube.
Question: What dataset is used for training Word2Vec in Italian language?
Evidence: The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences.
The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila).
Answer: The answers are shown as follows:
* extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila)