DataHammer/scimrc huggingface dataset:
Question: What is the objective of Events from Twitter Data?
Evidence: To illustrate that semantically different yet related applications may have significant potential for reuse of features, let us consider the problem of event extraction, which involves identifying occurrences of specific type of events or activities from raw text.
Towards that, we analysed published works on three different types of events in different domains as described next:
Objective of this study is to design a ML model for identifying if there exist mentions of one of the nine types of bio-molecular interactions in (publicly available) Biomedical data. To train SVM based classifier, authors use GENETAG database, which is a tagged corpus for gene/protein named entity recognition. BioNLP 2009 shared task test-set was used to estimate performance of the system. Further details can be found at BIBREF22.
Objective of the study was to design ML model for enabling automated detection of specific financial events in the news text. Ten diﬀerent types of ﬁnancial events were considered including announcements regarding CEOs, presidents, products, competitors, partners, subsidiaries, share values, revenues, proﬁts, and losses. To train and test SVM and CRF based ML models, authors used data set consisting of 200 news messages extracted from the Yahoo! Business and Technology newsfeeds, having financial events and relations manually annotated by 3 domain experts. Further details can be found at BIBREF23.
Objective of the study was to design an ML based system for extracting open domain calendar of significant events from Twitter-data. 38 different types of events were considered for designing the system. To train the ML model, an annotated corpus of 1000 tweets (containing 19,484 tokens) was used and trained model was tested on 100 million most recent tweets. Further details can be found at BIBREF24.
Table TABREF21 below depicts classes of features selected by authors of these works (as described in the corresponding references above) to highlight the point that despite domain differences, these applications share similar sets of features. Since authors of these works did not cite each other, it is possible that that these features might have been identified independently. This, in turn, supports the hypothesis that if adequate details of any one or two of these applications are fed to a system described in this work, which is designed to estimate semantic similarities across applications, system can automatically suggest potential features for consideration for the remaining applications to start with without requiring manual knowledge of the semantically related applications.
Objective of the study was to design an ML based system for extracting open domain calendar of significant events from Twitter-data.
Answer: Objective of the study was to design an ML based system for extracting open domain calendar of significant events from Twitter-data.
Question: Given these paragraphs about Asperger syndrome, what disorder has it merged into?
Evidence: Asperger syndrome (AS), also known as Asperger's, is a neurodevelopmental condition characterized by significant difficulties in social interaction and nonverbal communication, along with restricted and repetitive patterns of behaviour and interests. The syndrome is no longer recognised as a diagnosis in itself, having been merged with other conditions into autism spectrum disorder (ASD). It was considered to differ from other diagnoses that were merged into ASD by relatively unimpaired spoken language and intelligence.

The syndrome was named after the Austrian pediatrician Hans Asperger, who, in 1944, described children in his care who struggled to form friendships, did not understand others' gestures or feelings, engaged in one-sided conversations about their favourite interests, and were clumsy. In 1994, the diagnosis of Asperger's was included in the fourth edition (DSM-IV) of the American Diagnostic and Statistical Manual of Mental Disorders; however, with the publication of DSM-5 in 2013 the syndrome was removed, and the symptoms are now included within autism spectrum disorder along with classic autism and pervasive developmental disorder not otherwise specified (PDD-NOS). It was similarly merged into autism spectrum disorder in the International Classification of Diseases (ICD-11) as of 2021.

The exact cause of Asperger's is poorly understood. While it has high heritability, the underlying genetics have not been determined conclusively. Environmental factors are also believed to play a role. Brain imaging has not identified a common underlying condition. There is no single treatment, and the UK's National Health Service (NHS) guidelines suggest that 'treatment' of any form of autism should not be a goal, since autism is not 'a disease that can be removed or cured'. According to the Royal College of Psychiatrists, while co-occurring conditions might require treatment, 'management of autism itself is chiefly about the provision of the education, training and social support/care required to improve the person's ability to function in the everyday world'. The effectiveness of particular interventions for autism is supported by only limited data. Interventions may include social skills training, cognitive behavioral therapy, physical therapy, speech therapy, parent training, and medications for associated problems, such as mood or anxiety. Autistic characteristics tend to become less obvious in adulthood, but social and communication difficulties usually persist.

https://en.wikipedia.org/wiki/Asperger_syndrome
Answer: It has been merged with other conditions into autism spectrum disorder (ASD)
Question: Who is the director of "Who Killed Hanna Jane?"
Evidence: Who Killed Hannah Jane? is a 1984 Australian television film about the murder conviction of Arthur Peden directed by Peter Fisk and starring Graham Rouse, June Salter, Judi Farr, Edward Howell, and Barry Otto. It was the third of four telemovies called Verdict produced by the ABC dramatising real cases (the others being The Dean Case, The Amorous Dentist, and The Schippan Mystery)
Answer: Peter Fisk is the director of the film.
Question: What is the difference between soccer(football) and American football?
Answer: Soccer is played with a round ball that can be kicked and headed. American football, however, is more of a rugby type game in which the oblong shaped ball is thrown and passed as well as kicked.
Question: Name some Japanese words that describe various Japanese art forms.
Answer: Ikebana is the art of flower arranging. Kabuki is a form of traditional Japanese theatre. Origami is the art of folding paper into various shapes, such as animals. Anime is a Japanese form of comic book art. Haiku is a form of Japanese poetry.
Question: What is a sedentary way of living?
Answer: When we talk about sedentarism in a community, we're talking about a group of people who settle down in one place and build their economic, social, and cultural lives there. The change from being nomadic to staying in one place is what led to the first civilizations. When we say that someone has a sedentary lifestyle, we mean that they sit or lie down for a long time and don't move around much or at all.
Question: Which type of summary generated by their approach is preferred by evaluators?
Evidence: We considered a dataset of 100 employees, where for each employee multiple peer comments were recorded. Also, for each employee, a manual summary was generated by an HR personnel. The summaries generated by our ILP-based approach were compared with the corresponding manual summaries using the ROUGE BIBREF22 unigram score. For comparing performance of our ILP-based summarization algorithm, we explored a few summarization algorithms provided by the Sumy package. A common parameter which is required by all these algorithms is number of sentences keep in the final summary. ILP-based summarization requires a similar parameter K, which is automatically decided based on number of total candidate phrases. Assuming a sentence is equivalent to roughly 3 phrases, for Sumy algorithms, we set number of sentences parameter to the ceiling of K/3. Table TABREF51 shows average and standard deviation of ROUGE unigram f1 scores for each algorithm, over the 100 summaries. The performance of ILP-based summarization is comparable with the other algorithms, as the two sample t-test does not show statistically significant difference. Also, human evaluators preferred phrase-based summary generated by our approach to the other sentence-based summaries.
Also, human evaluators preferred phrase-based summary generated by our approach to the other sentence-based summaries.
Answer: Phrase-based summary.
Question: What should I get for my friends who love to cook?
Answer: Some thoughtful gifts for the avid home cooks include a sous vide, a nice skillet, a beautiful spatula, or a dutch oven. On a budget? You can consider unique condiments such as nice olive oil, chili crunch or spicy mayo.
Question: How does FTX collapse?
Answer: FTX was reported to move a significant portion of its assets to its partner firm Alameda Research in an article published by CoinDesk in November 2022. After this, FTX rival Binance's CEO Changpeng Zhao announced that Binance would sell its holdings of FTX tokens, which was quickly followed by a spike in customer withdrawals from FTX. FTX was unable to meet the demand for such customer withdrawals. On December 12, 2022, founder Sam Bankman-Fried was arrested by the Bahamian authorities for financial offences, at the request of the US government.
Question: is daylight saving necessary at all?
Answer: Absolutely no! Daylight Savings originated around WWI in an effort to conserve fuel and electricity. US enacted the act to preserve Daylight saving time in 1918 and established standard time zone across the States. However, with modern power grids and diverse night life, Daylight Savings is seemingly not relevant today. A lot of people complain about the inconvenience in turning the clock back and forth twice a year. The rate of being late for schedules or air flights on the day of daylight saving starts or ends every year is much higher than any other day in a year. In fact, even in the US, Arizona and Hawaii do not recognize daylight saving time. So do a lot of other countries like China and Japan. For European countries, the daylight saving switch date differs from one to another. All the chaos of daylight saving cause more trouble in schedule across-region appointments and meetings than any benefits of it if we can think of any.
Question: Classify each of the following as either names or objects: Mike, car, paper, Roger, wheel, Will, lamp, Clara, Fabiana, Peter.
Answer: Car, paper, wheel and lamp are objects. The rest are names.
Question: Why do my plants keep dying?
Answer: The most common causes is too much water (flood), followed by too little water (drought). If the plants are indoors, ensure they receive sufficient sunlight.
Question: Why are all the planets and moons round in shape? What makes them round? Does there exist another shape?
Answer: Not all planets and moons are round. There is some variety in their shapes depending on their masses, and curiously looking worlds might even exist, unlike anything we know from our system.

Everything within a planet or a moon is under the influence of gravity in such a way that bits of it would move to the center if they could. Under ideal conditions, it would acquire a spherical shape. For objects made of something other than liquids, it varies at what point they become round. It is generally assumed that they mostly become spheres around 400 kilometers/248 miles in diameter when made from icy material commonly found in our Solar System. Still, if something is made from something more rigid, it needs to be larger in diameter than that - around 750 km/466 miles. At a sufficient mass, even rock becomes fluid-like. The moon of Mars Deimos is only 12.6 km/7.8 miles across and is not spherical, and there are many other small moons of other planets that don’t have a round shape.

This, however, is not all. Astronomical objects spin, giving them the flattened shape of a sphere the faster their rotation if they are massive enough. On top of it, they can have moons. This causes a bulge to form at the position of their orbiting body around them. In the case of Earth, our Moon causes tides via this process. Also, our Sun gives our planet tides. Elsewhere in the Universe, there could be planets with massive moons or two similarly massive planets, called double planets, orbiting their common center of mass, the barycenter, at a close distance, which can cause even stronger bulging. Their shape would be affected this way more visibly.

Finally, a bizarre, toroidal planet, shown above, can be stable from what we know from physics, at least for a while. As I mentioned earlier, even rigid rock behaves like a fluid at large masses. An adequate rotation speed can counterbalance the strength of gravity with centrifugal acceleration, and a torus-shaped planet could exist. A possible scenario for such a world to come to be would be if two sufficiently fast rotating planets collided and formed a doughnut of vaporized rock, a so-called synestia.

We currently think that the events that would lead to the formation of a world of this shape are so unlikely that there might not be a single toroidal planet in the Visible Universe, but we were quite a few times surprised by what we discovered during our exploration of Cosmos. Perhaps we will even find a doughnut shape world one day.
Question: Identify which instrument is string or percussion: Gandingan, Psaltery
Answer: Psaltery is string, Gandingan is percussion.
Question: What human judgement metrics are used?
Evidence: We conduct a human evaluation to validate the effectiveness of the proposed multi-curricula learning framework. We employ the DailyDialog as the evaluation corpus since it is closer to our daily conversations and easier for humans to make the judgment. We randomly sampled 100 cases from the test set and compared the generated responses of the models trained with the vanilla learning procedure and the multi-curricula learning framework. Three annotators, who have no knowledge about which system the response is from, are then required to evaluate among win (response$_1$ is better), loss (response$_2$ is better) and tie (they are equally good or bad) independently, considering four aspects: coherence, logical consistency, fluency and diversity. Cases with different rating results are counted as “tie”. Table TABREF25 reveals the results of the subjective evaluation. We observe that our multi-curricula learning framework outperforms the vanilla training method on all the five dialogue models and the kappa scores indicate that the annotators came to a fair agreement in the judgment. We checked the cases on which the vanilla training method loses to our multi-curricula learning method and found that the vanilla training method usually leads to irrelevant, generic and repetitive responses, while our method effectively alleviates such defects.
Three annotators, who have no knowledge about which system the response is from, are then required to evaluate among win (response$_1$ is better), loss (response$_2$ is better) and tie (they are equally good or bad) independently, considering four aspects: coherence, logical consistency, fluency and diversity.
Answer: The answers are shown as follows:
* coherence, logical consistency, fluency and diversity
Question: Write a letter to Dear Abby on relationship advice.
Answer: Dear Abby,
I hope this letter finds you well. I am writing to seek your advice as my relationship with my partner has gotten to an unbearable stage in the last few weeks and is weighing heavy on my mind. We have been together for seventeen years now. We love each other deeply but have been struggling to connect on a deeper level. I want this relationship to work more than anything in the world, but I am unsure where to start. I have tried talking to him about my feelings, but he seems to shrug me off at any attempt. It feels like the rock has set in.I look forward to hearing your thoughts. 
Sincerely,
Mrs. Mavis.
Question: What is celiac disease?
Evidence: Coeliac disease (British English) or celiac disease (American English) is a long-term autoimmune disorder, primarily affecting the small intestine, where individuals develop intolerance to gluten, present in foods such as wheat, rye and barley. Classic symptoms include gastrointestinal problems such as chronic diarrhoea, abdominal distention, malabsorption, loss of appetite, and among children failure to grow normally. This often begins between six months and two years of age. Non-classic symptoms are more common, especially in people older than two years. There may be mild or absent gastrointestinal symptoms, a wide number of symptoms involving any part of the body, or no obvious symptoms. Coeliac disease was first described in childhood; however, it may develop at any age. It is associated with other autoimmune diseases, such as Type 1 diabetes mellitus and Hashimoto's thyroiditis, among others.
Answer: Celiac disease is an autoimmune disorder affecting the small intestine where the ingestion of gluten can lead to long-term serious health issues.
Question: Is the method described in this work a clustering-based method?
Evidence: We suggest a more advanced procedure of graph construction that uses the interpretability of vector addition and subtraction operations in word embedding space BIBREF6 while the previous algorithm only relies on the list of nearest neighbours in word embedding space. The key innovation of our algorithm is the use of vector subtraction to find pairs of most dissimilar graph nodes and construct the graph only from the nodes included in such “anti-edges”. Thus, our algorithm is based on graph-based word sense induction, but it also relies on vector-based operations between word embeddings to perform filtering of graph nodes. Analogously to the work of Pelevina:16, we construct a semantic relatedness graph from a list of nearest neighbours, but we filter this list using the following procedure:
We suggest a more advanced procedure of graph construction that uses the interpretability of vector addition and subtraction operations in word embedding space BIBREF6 while the previous algorithm only relies on the list of nearest neighbours in word embedding space.
Answer: Yes.
Question: Why is it difficult to calculate the inverse and Jacobian of an arbitrary neural network?
Evidence: For the projection we can use an arbitrary invertible function, and given the representational power of neural networks they seem a natural choice. However, calculating the inverse and Jacobian of an arbitrary neural network can be difficult, as it requires that all component functions be invertible and also requires storage of large Jacobian matrices, which is memory intensive. To address this issue, several recent papers propose specially designed invertible networks that are easily trainable yet still powerful BIBREF16 , BIBREF17 , BIBREF19 . Inspired by these works, we use the invertible transformation proposed by BIBREF16 , which consists of a series of “coupling layers”. This architecture is specially designed to guarantee a unit Jacobian determinant (and thus the invertibility property).
From Eq. ( EQREF22 ) we know that only INLINEFORM0 is required for accomplishing learning and inference; we never need to explicitly construct INLINEFORM1 . Thus, we directly define the architecture of INLINEFORM2 . As shown in Figure FIGREF24 , the nonlinear transformation from the observed embedding INLINEFORM3 to INLINEFORM4 represents the first coupling layer. The input in this layer is partitioned into left and right halves of dimensions, INLINEFORM5 and INLINEFORM6 , respectively. A single coupling layer is defined as: DISPLAYFORM0 
where INLINEFORM0 is the coupling function and can be any nonlinear form. This transformation satisfies INLINEFORM1 , and BIBREF16 show that its Jacobian matrix is triangular with all ones on the main diagonal. Thus the Jacobian determinant is always equal to one (i.e. volume-preserving) and the invertibility condition is naturally satisfied.
To be sufficiently expressive, we compose multiple coupling layers as suggested in BIBREF16 . Specifically, we exchange the role of left and right half vectors at each layer as shown in Figure FIGREF24 . For instance, from INLINEFORM0 to INLINEFORM1 the left subset INLINEFORM2 is unchanged, while from INLINEFORM3 to INLINEFORM4 the right subset INLINEFORM5 remains the same. Also note that composing multiple coupling layers does not change the volume-preserving and invertibility properties. Such a sequence of invertible transformations from the data space INLINEFORM6 to INLINEFORM7 is also called normalizing flow BIBREF20 .
However, calculating the inverse and Jacobian of an arbitrary neural network can be difficult, as it requires that all component functions be invertible and also requires storage of large Jacobian matrices, which is memory intensive.
Answer: Because it requires that all component functions be invertible and also requires storage of large Jacobian matrices, which is memory intensive.