DataHammer/scimrc huggingface dataset:
Question: What kind of framework do they propose?
Evidence: Automated deriving discourse relation from free text is a challenging but im-portant problem. The shallow discourse parsing is very useful in the text summariza-tion BIBREF0 , opinion analysis BIBREF1 and natural language generation. Shallow discourse parser is the system of parsing raw text into a set of discourse relations between two adjacent or non-adjacent text spans. Discourse relation is composed of a discourse connective, two arguments of the discourse connective and the sense of the discourse connective. Discourse connective signals the explicit dis-course relation, but in non-explicit discourse relation, a discourse connective is omit-ted. Two arguments of the discourse connective, Arg1 and Arg2, which are the two adjacent or non-adjacent text spans connecting in the discourse relation. The sense of the discourse connective characterizes the nature of the discourse relations. The following discourse relation annotation is taken from the document in the PDTB. Arg1 is shown in italicized, and Arg2 is shown in bold. The discourse connective is underlined.
The connective identifier finds the connective word, “unless”. The arguments identifier locates the two arguments of “unless”. The sense classifier labels the dis-course relation. The non-explicit identifier checks all the pair of adjacent sentences. If the non-explicit identifier indentifies the pair of sentences as non-explicit relation, it will label it the relation sense. Though many research work BIBREF2 , BIBREF3 , BIBREF4 are committed to the shallow discourse parsing field, all of them are focus on the subtask of parsing only rather than the whole parsing process. Given all that, a full shallow discourse parser framework is proposed in our paper to turn the free text into discourse relations set. The parser includes connective identifier, arguments identifier, sense classifier and non-explicit identifier, which connects with each other in pipeline. In order to enhance the performance of the parser, the feature-based maximum entropy model approach is adopted in the experiment. Maximum entropy model offers a clean way to combine diverse pieces of contextual evidence in order to estimate the probability of a certain linguistic class occurring with a certain linguistic context in a simple and accessible manner. The three main contributions of the paper are:
The rest of this paper is organized as follows. Section 2 reviews related work in discourse parsing. Section 3 describes the experimental corpus–PDTB. Section 4 de-scribes the framework and the components of the parser. Section 5 presents experi-ments and evaluations. Conclusions are presented in the Section 6.
Given all that, a full shallow discourse parser framework is proposed in our paper to turn the free text into discourse relations set
Answer: A shallow discourse parser framework.
Question: Give me an example of numbers in these categories:  Natural Numbers, Prime Number, Composite Number, Whole Numbers, Integers, Rational Numbers, Irrational Numbers, Real Numbers, Complex Number.
Answer: These are a few examples for each type of number:

Natural Numbers: 1, 2, 3, 4
Prime Number: 2, 3, 5, 7
Composite Number: 4, 6, 8, 9
Whole Numbers: 0, 1, 2, 3
Integers: -1, 0, 1, 2
Rational Numbers: −1/3, 0.99999…, 7/2, 13/9
Irrational Numbers: √5, √11, √21, π
Real Numbers: -2, 0, 1/2, √3
Complex Number: 0.39 + 1.9 i, √5 + √9i, −5 + πi, 2 + i
Question: Provide a bulleted summary of the history of how home-field advantage is determined for the World Series.
Evidence: Home-field advantage
World Series
The World Series used several different formats in its early years. Initially, it generally followed an alternating home-and-away pattern, except that if a seventh game was possible, its site was determined by coin toss prior to the sixth game. In 1924 the Series began using a 2-3-2 format, presumably to save on travel costs, a pattern that has continued to this day with the exception of a couple of the World War II years when wartime travel restrictions compelled a 3-4 format (used in 1943 and 1945, but not in the 1944 series, which was contested between crosstown rivals the St. Louis Browns and St. Louis Cardinals; all games were held in the same stadium in St. Louis). From the start of the 2-3-2 format through the 2002 season, home-field advantage generally alternated between leagues each year. Prior to the 1994 strike, the National League champion received home-field advantage in even-numbered years and the American League champion in odd-numbered years; these were reversed for 1995–2002 (because 1994 would have been the NL's turn to have home-field, but the World Series was canceled by the aforementioned strike). That changed starting in 2003.

The 2002 All-Star Game ended in a tie, much to the displeasure of both fans and sportswriters, who complained about a lack of intensity and competitiveness on the part of the players. This hit especially close to home for Commissioner Bud Selig, as the game had been played in his home city of Milwaukee, Wisconsin. In response, to make the game's outcome more meaningful, in 2003, MLB began assigning home-field advantage in the World Series to the winner of that year's All-Star Game, which is typically held in mid-July.

Following the acceptance of a new collective bargaining agreement after the 2016 season (which went into effect in 2017) home-field advantage in the World Series is no longer tied to the outcome of the All-Star Game, but instead is granted to the team with the better regular-season record. One exception was 2020, when all World Series games were played at a neutral site. The home-field advantage designation in the World Series was determined based on whichever pennant winner held the higher seed in its league, not necessarily the team with the better regular season record. However, in the event both pennant winners had held the same seeding number, only then regular season records would have determined the home team in Games 1, 2, 6, and 7.
Answer: Home field advantage for the World Series has been determined in the following ways throughout history:
- 1924 - 2002: Home field advantage simply alternated between the American and National Leagues each year.
- 2002 - 2016: Home field advantage was determined by the winner of the All-Star game.
- 2016 - Present: Home field advantage is determined by the team with the best record playing in the World Series.
Question: Q: What is an emergency use authorization and how is it being used to respond to COVID-19?
Answer: A: An emergency use authorization (EUA) is a mechanism to facilitate the availability and use of medical countermeasures, including vaccines, tests, and medicines, during public health emergencies, such as the current COVID-19 pandemic. Under an EUA, the FDA may allow the use of unapproved medical products, or unapproved uses of approved medical products in an emergency to diagnose, treat, or prevent serious or life-threatening diseases or conditions when certain statutory criteria have been met, including that there are no adequate, approved, and available alternatives. Taking into consideration input from the FDA, manufacturers decide whether and when to submit an EUA request to the FDA.

Learn more about EUAs in this videoExternal Link Disclaimer
Read more about COVID-19 tests 
Find a community-based testing site
Question: What is an option in finance?
Answer: In finance, an option is a contract which conveys to its owner, the holder, the right, but not the obligation, to buy or sell a specific quantity of an underlying asset or instrument at a specified strike price on or before a specified date, depending on the style of the option. Options are typically acquired by purchase, as a form of compensation, or as part of a complex financial transaction. Thus, they are also a form of asset and have a valuation that may depend on a complex relationship between underlying asset price, time until expiration, market volatility, the risk-free rate of interest, and the strike price of the option.
Question: What is the key aspect of the low dimensionality representation (LDR)?
Evidence: The key aspect of the low dimensionality representation (LDR) is the use of weights to represent the probability of each term to belong to each one of the different language varieties. We assume that the distribution of weights for a given document should be closer to the weights of its corresponding language variety. Formally, the LDR is estimated as follows:
The key aspect of the low dimensionality representation (LDR) is the use of weights to represent the probability of each term to belong to each one of the different language varieties.
Answer: The key aspect of the low dimensionality representation (LDR) is the use of weights to represent the probability of each term to belong to each one of the different language varieties.
Question: Why is there an Irish pub everywhere you go?
Answer: There are many reasons in history for Irish pubs being situated in obscure towns, places and holiday destinations. One reason is due to the amount of Irish people that immigrated from the 1500's right up too late 1980's for various reasons be it famine, civil war or opportunity. This resulted in Irish people settling and developing communities that included Irish pubs that in turn attracted customers and were successful. In more recent times, there is a holding company that has placed Irish pubs in obscure areas i.e. on some of the highest mountains in the world or small villages with small populations to build on the global phenomenon of 'there is always an irish pub everywhere you go'.
Question: What are the contributions of this paper?
Evidence: It is well-established that gender bias exists in language – for example, we see evidence of this given the prevalence of sexism in abusive language datasets BIBREF0, BIBREF1. However, these are extreme cases of gender norms in language, and only encompass a small proportion of speakers or texts.
Less studied in NLP is how gender norms manifest in everyday language – do people talk about women and men in different ways? These types of differences are far subtler than abusive language, but they can provide valuable insight into the roots of more extreme acts of discrimination. Subtle differences are difficult to observe because each case on its own could be attributed to circumstance, a passing comment or an accidental word. However, at the level of hundreds of thousands of data points, these patterns, if they do exist, become undeniable. Thus, in this work, we introduce new datasets and methods so that we can study subtle gender associations in language at the large-scale.
Our contributions include:
Two datasets for studying language and gender, each consisting of over 300K sentences.
Methods to infer gender-associated words and labeled clusters in any domain.
Novel findings that demonstrate in both domains that people do talk about women and men in different ways.
Each contribution brings us closer to modeling how gender associations appear in everyday language. In the remainder of the paper, we present related work, our data collection, methods and findings, and human evaluations of our system.
Our contributions include: • Two datasets for studying language and gender, each consisting of over 300K sentences. • Methods to infer gender-associated words and labeled clusters in any domain. • Novel findings that demonstrate in both domains that people do talk about women and men in different ways.
Answer: • Two datasets for studying language and gender, each consisting of over 300K sentences. • Methods to infer gender-associated words and labeled clusters in any domain. • Novel findings that demonstrate in both domains that people do talk about women and men in different ways.
Question: 5 origami styles
Answer: Book fold 
Kite fold
Cupboard fold
Shawl fold 
Cushion fold
Question: Do they specify the model they use for Gunrock?
Evidence: Figure FIGREF3 provides an overview of Gunrock's architecture. We extend the Amazon Conversational Bot Toolkit (CoBot) BIBREF6 which is a flexible event-driven framework. CoBot provides ASR results and natural language processing pipelines through the Alexa Skills Kit (ASK) BIBREF7. Gunrock corrects ASR according to the context (asr) and creates a natural language understanding (NLU) (nlu) module where multiple components analyze the user utterances. A dialog manager (DM) (dm) uses features from NLU to select topic dialog modules and defines an individual dialog flow. Each dialog module leverages several knowledge bases (knowledge). Then a natural language generation (NLG) (nlg) module generates a corresponding response. Finally, we markup the synthesized responses and return to the users through text to speech (TTS) (tts). While we provide an overview of the system in the following sections, for detailed system implementation details, please see the technical report BIBREF1.
We extend the Amazon Conversational Bot Toolkit (CoBot) BIBREF6 which is a flexible event-driven framework. CoBot provides ASR results and natural language processing pipelines through the Alexa Skills Kit (ASK) BIBREF7. Gunrock corrects ASR according to the context (asr) and creates a natural language understanding (NLU) (nlu) module where multiple components analyze the user utterances. A dialog manager (DM) (dm) uses features from NLU to select topic dialog modules and defines an individual dialog flow. Each dialog module leverages several knowledge bases (knowledge). Then a natural language generation (NLG) (nlg) module generates a corresponding response.
While we provide an overview of the system in the following sections, for detailed system implementation details, please see the technical report BIBREF1.
Answer: No.
Question: What does the dataset UIT-ViIC contain?
Evidence: Image Captioning, the task of automatic generation of image captions, has attracted attentions from researchers in many fields of computer science, being computer vision, natural language processing and machine learning in recent years. This paper contributes to research on Image Captioning task in terms of extending dataset to a different language - Vietnamese. So far, there is no existed Image Captioning dataset for Vietnamese language, so this is the foremost fundamental step for developing Vietnamese Image Captioning. In this scope, we first build a dataset which contains manually written captions for images from Microsoft COCO dataset relating to sports played with balls, we called this dataset UIT-ViIC. UIT-ViIC consists of 19,250 Vietnamese captions for 3,850 images. Following that, we evaluate our dataset on deep neural network models and do comparisons with English dataset and two Vietnamese datasets built by different methods. UIT-ViIC is published on our lab website for research purposes.
In this scope, we first build a dataset which contains manually written captions for images from Microsoft COCO dataset relating to sports played with balls, we called this dataset UIT-ViIC.
Answer: It is a dataset which contains manually written captions for images from Microsoft COCO dataset relating to sports played with balls.
Question: From the passage provided, extract where Java was developed
Evidence: Java is a high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible. It is a general-purpose programming language intended to let programmers write once, run anywhere (WORA), meaning that compiled Java code can run on all platforms that support Java without the need to recompile. Java applications are typically compiled to bytecode that can run on any Java virtual machine (JVM) regardless of the underlying computer architecture. The syntax of Java is similar to C and C++, but has fewer low-level facilities than either of them. The Java runtime provides dynamic capabilities (such as reflection and runtime code modification) that are typically not available in traditional compiled languages. As of 2019, Java was one of the most popular programming languages in use according to GitHub,[citation not found] particularly for client–server web applications, with a reported 9 million developers.

Java was originally developed by James Gosling at Sun Microsystems. It was released in May 1995 as a core component of Sun Microsystems' Java platform. The original and reference implementation Java compilers, virtual machines, and class libraries were originally released by Sun under proprietary licenses. As of May 2007, in compliance with the specifications of the Java Community Process, Sun had relicensed most of its Java technologies under the GPL-2.0-only license. Oracle offers its own HotSpot Java Virtual Machine, however the official reference implementation is the OpenJDK JVM which is free open-source software and used by most developers and is the default JVM for almost all Linux distributions.

As of March 2023, Java 20 is the latest version, while Java 17, 11 and 8 are the current long-term support (LTS) versions.
Answer: Java was originally developed by James Gosling at Sun Microsystems.
Question: What are good native plants for a garden in Ohio?
Answer: Native plants that will grow well in Ohio include Purple Coneflower, Thin Leaf Mountain Mint, Sawtooth Sunflower, and Little Bluestem Grass. There's quite a few plants that grow well in Ohio and are native to the area. The Missouri Botanical Garden has great resources on those kinds of plants as does the Lady Bird Johnson Wildflower Center.
Question: What's the two-fold input provided by X-stance?
Evidence: The input provided by x-stance is two-fold: (A) a natural language question concerning a political issue; (B) a natural language commentary on a specific stance towards the question.
The label to be predicted is either `favor' or `against`. This corresponds to a standard established by BIBREF0. However, x-stance differs from that dataset in that it lacks a `neither' class; all comments refer to either a `favor' or an `against` position. The task posed by x-stance is thus a binary classification task.
As an evaluation metric we report the macro-average of the F1-score for `favor' and the F1-score for `against', similar to BIBREF9. We use this metric mainly to strengthen comparability with the previous benchmarks.
The input provided by X-stance is two-fold:(A) a natural language question concerning a political issue; (B) a natural language commentary on a specific stance towards the question.
Answer: (A) a natural language question concerning a political issue; (B) a natural language commentary on a specific stance towards the question.
Question: Do the following activities typically happen at night or during the day? A hike in a park, stargazing, going to a beach tidepool, dinner at a restaurant, looking for shooting stars, working a desk job
Answer: A hike in a park: daytime, stargazing: nighttime, going to a beach tidepool: daytime, dinner at a restaurant: nighttime, looking for shooting stars: nighttime, working a desk job: daytime
Question: Did they use a relation extraction method to construct the edges in the graph?
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: No.
Question: What are the benefits of minimally-invasive orthopedic surgery?
Answer: There are several reasons why an orthopedic surgeon would want to choose a minimally-invasive surgical option as opposed to an open procedure. On the one hand: minimally invasive operations tend to have faster recovery times, less blood loss, and smaller incisions (which is good from an aesthetic perspective as it means smaller scars). On the other hand, minimally invasive options might not provide as much visibility into the surgical site as an open incision would, and depending on the procedure might not promote the type of fusion required. Of course, any surgical decision should be made by a trained and qualified physician and will depend entirely on the specific circumstances presented by the patient.
Question: What is Intermittent Fasting?
Evidence: Intermittent fasting is any of various meal timing schedules that cycle between voluntary fasting (or reduced calorie intake) and non-fasting over a given period. Methods of intermittent fasting include alternate-day fasting, periodic fasting, and daily time-restricted feeding.
Answer: Intermittent fasting is a time-restricted form of meal scheduling to reduce caloric intake by voluntarily abstaining from eating during defined periods. Various fasting methods include daily feeding during restricted time windows, alternate-day fasting, and periodic fasting.
Question: "Big Bang": what does it mean?
Answer: The Big Bang theory, which is how astronomers explain the origin of the universe, holds that the universe as we know it began billions of years ago in a huge explosion. At the time of this explosion, all the matter that exists in the universe today was concentrated at one point.

From the moment of the explosion, matter began to expand and is still expanding today. That is why the Big Bang theory includes the theory of the expanding universe. In addition to expanding, matter began to form more complex structures, from atoms and molecules to the living organisms we know today.

The name "Big Bang" was first used by physicist Fred Hoyle in 1949, who disagreed with this theory, preferring the steady-state model of the universe.  The idea of a big bang seemed absurd to Hoyle.
Question: What are Riot Games' most popular games?
Answer: League of Legends, Valorant, Teamfight Tactics
Question: Based on the following passage, what are the environmental consequences of fracking?
Evidence: The potential environmental effects of hydraulic fracturing include air emissions and climate change, high water consumption, groundwater contamination, land use, risk of earthquakes, noise pollution, and various health effects on humans. Air emissions are primarily methane that escapes from wells, along with industrial emissions from equipment used in the extraction process. Modern UK and EU regulation requires zero emissions of methane, a potent greenhouse gas. Escape of methane is a bigger problem in older wells than in ones built under more recent EU legislation.
Answer: The environmental effects of fracking include air emissions (primarily methane), groundwater contamination, high water consumption, groundwater contamination, land use, noise pollution, risk of earthquakes, and health effects on humans.