DataHammer/scimrc huggingface dataset:
Question: how do they collect the comparable corpus?
Evidence: Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Twitter activity consists of short posts called tweets which are limited to 140 characters. Retweets, where users repost a tweet to spread its content, were not considered. (The spread of causal statements will be considered in future work.) We considered only English-language tweets for this study. To avoid cross-language effects, we kept only tweets with a user-reported language of `English' and, as a second constraint, individual tweets needed to match more English stopwords than any other language's set of stopwords. Stopwords considered for each language were determined using NLTK's database BIBREF29 . A tweet will be referred to as a `document' for the rest of this work.
Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. The word `cause' was not included due to its use as a popular contraction for `because'. One `cause-word' per document restricted the analysis to single relationships between two relata. Documents that contain bidirectional words (`associate', `relate', `connect', `correlate', and any of their stems) were also not selected for analysis. This is because our focus is on causality, an inherently one-sided relationship between two objects. We also did not consider additional synonyms of these cause words, although that could be pursued for future work. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. The end result of this procedure identified 965,560 causal and 965,560 control documents. Each of the three “cause-words”, `caused', `causes', and `causing' appeared in 38.2%, 35.0%, and 26.8% of causal documents, respectively.
Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API.
Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present.
Answer: Randomly from a Twitter dump
Question: Give me a list of ski and snowboard apparel brands I have to buy
Answer: - Flylow
- Helly Hansen 
- Arc'Teryx
- Patagonia
Question: Why do we have winter and summer clocks?
Answer: Even today, this remains the rationale behind the clock change: aligning citizens' waking hours to daylight hours to save the energy spent on lighting. There are more daylight hours in the summer, and it is preferable to get up earlier so as not to waste precious daylight time while sleeping.
Question: What is the Carolina Hall
Evidence: Carolina Hall, formerly known as Saunders Hall, is a building on the University of North Carolina at Chapel Hill campus in Chapel Hill, North Carolina, in the United States. Carolina Hall was built in 1922 and named for William L. Saunders, an alumnus and a colonel in the Confederate States Army during the American Civil War. The name was changed to "Carolina Hall" in 2015.
Answer: The Carolina Hall, formerly known as Saunders Hall, is a building on the University of North Carolina at Chapel Hill campus in Chapel Hill, North Carolina, in the United States. Carolina Hall was built in 1922 and named for William L. Saunders, an alumnus and a colonel in the Confederate States Army during the American Civil War. The name was changed to "Carolina Hall" in 2015.
Question: I'd like a list of the last five Prime Ministers of New Zealand. Give it to me in a bulleted list with the format <name> (<start date> - <end date>)
Answer: Here is a list of the last five Prime Ministers of New Zealand:
- Chris Hipkins (25 January 2023 - now)
- Jacinda Ardern (26 October 2017 - 25 January 2023)
- Bill English (12 December 2016 - 26 October 2017)
- John Key (19 November 2008 - 12 December 2016)
- Helen Clark (10 December 1999 - 19 November 2008)
Question: What are the top three most popular collectible cardgames?
Answer: 1. Magic:  The Gathering
2. Pokemon
3. Yu-Gi-Oh!
Question: Classify each of the following European countries as either members of the European Union or non-members of the European Union: Spain, Albania, the United Kingdom, Ireland, Romania, Germany.
Answer: Germany, Spain, Ireland, and Romania are all members of the European Union.  Of these countries, Romania joined the EU most recently.  Albania and the United Kingdom are not members of the European Union.  It applied for EU membership on April 28, 2009 and since June 2014 has been an official candidate.  The United Kingdom was previously an EU member, but withdrew its membership in 2020.
Question: Extract the occupations of Elie Wiesel from the text.
Evidence: Elie Wiesel (/ˈɛli viːˈzɛl/, born Eliezer Wiesel, Yiddish: אליעזר װיזעל Eliezer Vizel; September 30, 1928 – July 2, 2016) was a Romanian-born American writer, professor, political activist, Nobel laureate, and Holocaust survivor.
Answer: Writer, Professor, Activist
Question: Is Killing is Sin ? Is it ture
Answer: Killing a human being should not be sin because same goes to any animal. So either both are sin or any one of it is not should not be accepted.
Question: Extract what countries border Greece based on the below text and separate them with a comma
Evidence: Greece, officially the Hellenic Republic, is a country in Southeast Europe. It is situated on the southern tip of the Balkan Peninsula, and is located at the crossroads of Europe, Asia, and Africa. Greece shares land borders with Albania to the northwest, North Macedonia and Bulgaria to the north, and Turkey to the northeast. The Aegean Sea lies to the east of the mainland, the Ionian Sea to the west, and the Sea of Crete and the Mediterranean Sea to the south. Greece has the longest coastline on the Mediterranean Basin, featuring thousands of islands. The country consists of nine traditional geographic regions, and has a population of approximately 10.5 million. Athens is the nation's capital and largest city, followed by Thessaloniki and Patras.
Answer: Albania, North Macedonia, Bulgaria and Turkey
Question: Can dogs talk?
Answer: Dogs can't talk in the same ways humans can, but there are able to communicate with each other using both sounds and body movements. They learn this from a young age.
Question: How many comments were used?
Evidence: We analyze the performance of the proposed method under the semi-supervised setting. We train the supervised IR model with different numbers of paired data. Figure FIGREF39 shows the curve (blue) of the recall1 score. As expected, the performance grows as the paired dataset becomes larger. We further combine the supervised IR with our unsupervised model, which is trained with full unpaired data (4.8M) and different number of paired data (from 50K to 4.8M). It shows that IR+Proposed can outperform the supervised IR model given the same paired dataset. It concludes that the proposed model can exploit the unpaired data to further improve the performance of the supervised model.
We further combine the supervised IR with our unsupervised model, which is trained with full unpaired data (4.8M) and different number of paired data (from 50K to 4.8M).
Answer: The answers are shown as follows:
* from 50K to 4.8M
Question: What tasks are involved in the model evaluation?
Evidence: We evaluated our models on 3 different datasets:
CSAT dataset for CSAT prediction, consisting of spoken transcripts (automatic via ASR).
20 newsgroups for topic identification task, consisting of written text;
Fisher Phase 1 corpus for topic identification task, consisting of spoken transcripts (manual);
We evaluated our models on 3 different datasets: • CSAT dataset for CSAT prediction, consisting of spo ken transcripts (automatic via ASR). • 20 newsgroups for topic identification task, consisting of written text; • Fisher Phase 1 corpus for topic identification task, consisting of spoken transcripts (manual);
Answer: CSAT, 20 newsgroups, and Fisher.
Question: What are best novels in Spanish?
Answer: One Hundred Years of Solitude
A Heart So White
The House of the Spirits
Don Quixote
Question: Is data security an illusion?
Evidence: Crypto AG was a Swiss company specialising in communications and information security founded by Boris Hagelin in 1952. The company was secretly purchased for US $5.75 million and jointly owned by the American Central Intelligence Agency (CIA) and West German Federal Intelligence Service (BND) from 1970 until about 1993, with the CIA continuing as sole owner until about 2018. The mission of breaking encrypted communication using a secretly owned company was known as "Operation Rubikon". With headquarters in Steinhausen, the company was a long-established manufacturer of encryption machines and a wide variety of cipher devices.
Answer: The long answer is yes.
Question: Given the text about the 2019 Masters Tournament, tell me who won the golf tournament, where the tournament was held and what age the winner was?
Evidence: The 2019 Masters Tournament was the 83rd edition of the Masters Tournament and the first of golf's four major championships in 2019, held between April 11 and 14 at Augusta National Golf Club in Augusta, Georgia.

Tiger Woods won his fifth Green Jacket, and fifteenth major, by one stroke ahead of three runners-up. It left him one shy of Jack Nicklaus's record six Masters wins, and three short of his record eighteen major wins. At age 43, he became the eleventh oldest player to win a major, and the second oldest Masters winner, again only bettered by Nicklaus who won at age 46. It was 11 years after his last major win, the 2008 U.S. Open, and 14 years since his last Masters win beating the previous record of 13 years held by Gary Player. Due to the high profile of Woods, and his storied fall from the top of the game due to personal issues and injuries, the victory generated a large amount of publicity around the world, and is regarded as one of the great sporting comebacks.
Answer: Tiger Woods won the golf tournament at Augusta National Golf Club in Augusta, GA and was 43 years old.
Question: What is the difference between a dialect and an accent?
Answer: Most people would commonly refer to an accent as a distinctly unique annunciation of consonant and vowels commonly heard in a geographic proximity or spoken by a distinct community coming from a geographic proximity. Dialect, on the other hand, has attributions beyond annunciation of sounds, and includes the use of slang or local terms, mixed etymology and an element of shared ideas or principles about how to communicate. Another category not considered here is a creole, which is a blending of two or more languages.
Question: What is a good way to get around without a car?
Answer: If you live in a city you can access public transport such as buses and trains. You can also walk, use a bicycle, or electric scooter to get around.
Question: Classify each of the following as either a single digit or a double digit number: 8, 4, 1, 12, 87, 65, 3, 5, 45, 97, 6, 2, 34, 71, 9, 48, 0, 7, 31, 50.
Answer: Single digits include: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.
Double digits include: 12, 31, 34, 45, 48, 50, 65, 71, 87, 97.
Question: Can this adversarial approach be used to directly improve model accuracy?
Evidence: To evaluate this application, we inject random triples into the graph, and measure the ability of to detect the errors using our optimization. We consider two types of incorrect triples: 1) incorrect triples in the form of $\langle s^{\prime }, r, o\rangle $ where $s^{\prime }$ is chosen randomly from all of the entities, and 2) incorrect triples in the form of $\langle s^{\prime }, r^{\prime }, o\rangle $ where $s^{\prime }$ and $r^{\prime }$ are chosen randomly. We choose 100 random triples from the observed graph, and for each of them, add an incorrect triple (in each of the two scenarios) to its neighborhood. Then, after retraining DistMult on this noisy training data, we identify error triples through a search over the neighbors of the 100 facts. The result of choosing the neighbor with the least influence on the target is provided in the Table 7 . When compared with baselines that randomly choose one of the neighbors, or assume that the fact with the lowest score is incorrect, we see that outperforms both of these with a considerable gap, obtaining an accuracy of $42\%$ and $55\%$ in detecting errors.
When compared with baselines that randomly choose one of the neighbors, or assume that the fact with the lowest score is incorrect, we see that outperforms both of these with a considerable gap, obtaining an accuracy of $42\%$ and $55\%$ in detecting errors.
Answer: Yes.
Question: What is the best song from Rihanna
Answer: 1. Love the way you lie 
2. Unfaithful
Question: Who wrote Jane Eyre?
Evidence: Swarcliffe Hall is a large hall that was constructed in 1800 in Birstwith, near Harrogate, England. The current house was built by John Greenwood in 1850, who engaged Major Rohde Hawkins as his architect, and is a Grade II listed building.

The original Swarcliffe Hall was built on the site c1800 by the Blessard family, however the current hall which was built by the Greenwood family was completed in 1850. Charlotte Brontë was employed as a governess at the hall in 1839 and it is said her time spent there helped inspire her novel Jane Eyre.

Prince Albert Victor, Duke of Clarence and Avondale visited the hall in 1888 and again during the following year whilst opening the New Bath Hospital in nearby Harrogate.
Answer: Charlotte Brontë
Question: Who are the annotators of this dataset?
Evidence: The proliferation of the online social media has lately resulted in the democratization of online content sharing. Among other media, Twitter is very popular for research and application purposes due to its scale, representativeness and ease of public access to its content. However, tweets, that are short messages of up to 140 characters, pose several challenges to traditional Natural Language Processing (NLP) systems due to the creative use of characters and punctuation symbols, abbreviations ans slung language.
Named Entity Recognition (NER) is a fundamental step for most of the information extraction pipelines. Importantly, the terse and difficult text style of tweets presents serious challenges to NER systems, which are usually trained using more formal text sources such as newswire articles or Wikipedia entries that follow particular morpho-syntactic rules. As a result, off-the-self tools trained on such data perform poorly BIBREF0 . The problem becomes more intense as the number of entities to be identified increases, moving from the traditional setting of very few entities (persons, organization, time, location) to problems with more. Furthermore, most of the resources (e.g., software tools) and benchmarks for NER are for text written in English. As the multilingual content online increases, and English may not be anymore the lingua franca of the Web. Therefore, having resources and benchmarks in other languages is crucial for enabling information access worldwide.
In this paper, we propose a new benchmark for the problem of NER for tweets written in French. The tweets were collected using the publicly available Twitter API and annotated with 13 types of entities. The annotators were native speakers of French and had previous experience in the task of NER. Overall, the generated datasets consists of INLINEFORM0 tweets, split in training and test parts.
The paper is organized in two parts. In the first, we discuss the data preparation steps (collection, annotation) and we describe the proposed dataset. The dataset was first released in the framework of the CAp 2017 challenge, where 8 systems participated. Following, the second part of the paper presents an overview of baseline systems and the approaches employed by the systems that participated. We conclude with a discussion of the performance of Twitter NER systems and remarks for future work.
The annotators were native speakers of French and had previous experience in the task of NER.
Answer: The annotators were native speakers of French and had previous experience in the task of NER.
Question: What should I do if I'm feeling depressed?
Answer: It's completely normal to feel depressed from time to time. Life is hard, and sometimes it just seems meaningless. One thing you can do in this situation is to try something new that you haven't done before, like taking a walk in a neighborhood where you haven't been, hang out with a friend that you haven't talked to for awhile, taking a vacation and traveling to somewhere new, or even just reading a new book or watching a new movie. New experiences can give you new perspectives in unexpected ways.
Question: What are fun activities to do with a toddler in Los Angeles?
Answer: There are a variety of fun things to do with a toddler in the Los Angeles area. To keep it casual, you may choose to visit parks that have playgrounds including Griffith Park or Pan Pacific Park or children's museums such as Cayton Children's Museum in Santa Monica or Discovery Cube Los Angeles in Sylmer. Mall areas like The Americana at Brand or Westfield Century City provide shops where toddlers can look around and play such as the CAMP store or Barnes and Nobles as well as restaurants and dessert shops that are kid-friendly. If you are planning in advance and are looking for more of a full day activity, visiting Disneyland in Anaheim or the beach at the Santa Monica Pier are also great options!
Question: What are some popular movies from the 1980s?
Answer: Some popular movies that were released in the 1980s are Sixteen Candles, The Breakfast Club, Pretty in Pink, Scarface, The Outsiders, Heathers, Stand By Me, ET, The Princess Bride, The Terminator, Aliens, Friday the 13th, St. Elmo's Fire, Children of the Corn, Grease 2, Purple Rain, Clue, A Nightmare on Elm Street, Lucas, Gremlins, Better Off Dead and Some Kind of Wonderful
Question: What model do they train to obtain shallow syntactic annotations on a large corpus?
Evidence: Base phrase chunking is a cheap sequence-labeling–based alternative to full syntactic parsing, where the sequence consists of non-overlapping labeled segments (Fig. FIGREF1 includes an example.) Full syntactic trees can be converted into such shallow syntactic chunk sequences using a deterministic procedure BIBREF9. BIBREF12 offered a rule-based transformation deriving non-overlapping chunks from phrase-structure trees as found in the Penn Treebank BIBREF13. The procedure percolates some syntactic phrase nodes from a phrase-syntactic tree to the phrase in the leaves of the tree. All overlapping embedded phrases are then removed, and the remainder of the phrase gets the percolated label—this usually corresponds to the head word of the phrase.
In order to obtain shallow syntactic annotations on a large corpus, we train a BiLSTM-CRF model BIBREF14, BIBREF15, which achieves 97% $F_1$ on the CoNLL 2000 benchmark test set. The training data is obtained from the CoNLL 2000 shared task BIBREF12, as well as the remaining sections (except §23 and §20) of the Penn Treebank, using the official script for chunk generation. The standard task definition from the shared task includes eleven chunk labels, as shown in Table TABREF4.
The size of the shallow syntactic feature embedding was 50 across all experiments, initialized uniform randomly.
All model implementations are based on the AllenNLP library BIBREF33.
In order to obtain shallow syntactic annotations on a large corpus, we train a BiLSTM-CRF model (Lample et al.,2016; Peters et al., 2017), which achieves 97% F1 on the CoNLL 2000 benchmark test set.
Answer: BiLSTM-CRF model (Lample et al.,2016; Peters et al., 2017).
Question: When buying a whole fish, how do you pick a good fish?
Answer: To pick a good, fresh fish, I would look for three characteristics: clear eyes, red gills, and firm flesh. Fresh fish generally have clear and not bloody eyes, gills that have a relatively brighter red color. In addition, I would also poke the fish to see if their flesh feels firm to the touch.
Question: Who is the youngest Stark child in Game of Thrones TV show?
Answer: Rickon Stark is the youngest Stark
Question: What are examples of famous plays written by William Shakespeare?
Answer: Famous plays written by William Shakespeare include Romeo and Juliet, Hamlet, King Lear, and Macbeth.
Question: Is this the first paper to propose a joint model for event and temporal relation extraction?
Evidence: As far as we know, all existing systems treat this task as a pipeline of two separate subtasks, i.e., event extraction and temporal relation classification, and they also assume that gold events are given when training the relation classifier BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. Specifically, they built end-to-end systems that extract events first and then predict temporal relations between them (Fig. FIGREF1). In these pipeline models, event extraction errors will propagate to the relation classification step and cannot be corrected afterwards. Our first contribution is the proposal of a joint model that extracts both events and temporal relations simultaneously (see Fig. FIGREF1). The motivation is that if we train the relation classifier with NONE relations between non-events, then it will potentially have the capability of correcting event extraction mistakes. For instance in Fig. FIGREF1, if the relation classifier predicts NONE for (Hutu, war) with a high confidence, then this is a strong signal that can be used by the event classifier to infer that at least one of them is not an event.
As far as we know, all existing systems treat this task as a pipeline of two separate subtasks, i.e., event extraction and temporal relation classification, and they also assume that gold events are given when training the relation classifier BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. Specifically, they built end-to-end systems that extract events first and then predict temporal relations between them (Fig. FIGREF1). In these pipeline models, event extraction errors will propagate to the relation classification step and cannot be corrected afterwards. Our first contribution is the proposal of a joint model that extracts both events and temporal relations simultaneously (see Fig. FIGREF1).
Answer: Yes.
Question: What is the seed lexicon?
Evidence: The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types.
The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event.
Answer: The answers are shown as follows:
* seed lexicon consists of positive and negative predicates