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id stringlengths 40 40	pid stringlengths 42 42	input stringlengths 8.37k 169k	output stringlengths 1 1.63k
753990d0b621d390ed58f20c4d9e4f065f0dc672	753990d0b621d390ed58f20c4d9e4f065f0dc672_0	Q: What is the seed lexicon? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is imp...	a vocabulary of positive and negative predicates that helps determine the polarity score of an event
753990d0b621d390ed58f20c4d9e4f065f0dc672	753990d0b621d390ed58f20c4d9e4f065f0dc672_1	Q: What is the seed lexicon? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is imp...	seed lexicon consists of positive and negative predicates
9d578ddccc27dd849244d632dd0f6bf27348ad81	9d578ddccc27dd849244d632dd0f6bf27348ad81_0	Q: What are the results? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is importa...	Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA+CO -...
02e4bf719b1a504e385c35c6186742e720bcb281	02e4bf719b1a504e385c35c6186742e720bcb281_0	Q: How are relations used to propagate polarity? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding aff...	based on the relation between events, the suggested polarity of one event can determine the possible polarity of the other event
02e4bf719b1a504e385c35c6186742e720bcb281	02e4bf719b1a504e385c35c6186742e720bcb281_1	Q: How are relations used to propagate polarity? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding aff...	cause relation: both events in the relation should have the same polarity; concession relation: events should have opposite polarity
44c4bd6decc86f1091b5fc0728873d9324cdde4e	44c4bd6decc86f1091b5fc0728873d9324cdde4e_0	Q: How big is the Japanese data? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is...	7000000 pairs of events were extracted from the Japanese Web corpus, 529850 pairs of events were extracted from the ACP corpus
44c4bd6decc86f1091b5fc0728873d9324cdde4e	44c4bd6decc86f1091b5fc0728873d9324cdde4e_1	Q: How big is the Japanese data? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is...	The ACP corpus has around 700k events split into positive and negative polarity
86abeff85f3db79cf87a8c993e5e5aa61226dc98	86abeff85f3db79cf87a8c993e5e5aa61226dc98_0	Q: What are labels available in dataset for supervision? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understan...	negative, positive
c029deb7f99756d2669abad0a349d917428e9c12	c029deb7f99756d2669abad0a349d917428e9c12_0	Q: How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usuall...	3%
39f8db10d949c6b477fa4b51e7c184016505884f	39f8db10d949c6b477fa4b51e7c184016505884f_0	Q: How does their model learn using mostly raw data? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding...	by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity
d0bc782961567dc1dd7e074b621a6d6be44bb5b4	d0bc782961567dc1dd7e074b621a6d6be44bb5b4_0	Q: How big is seed lexicon used for training? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affect...	30 words
a592498ba2fac994cd6fad7372836f0adb37e22a	a592498ba2fac994cd6fad7372836f0adb37e22a_0	Q: How large is raw corpus used for training? Text: Introduction Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affect...	100 million sentences
3a9d391d25cde8af3334ac62d478b36b30079d74	3a9d391d25cde8af3334ac62d478b36b30079d74_0	Q: Does the paper report macro F1? Text: 1.1em ::: 1.1.1em ::: ::: 1.1.1.1em Thomas Haider$^{1,3}$, Steffen Eger$^2$, Evgeny Kim$^3$, Roman Klinger$^3$, Winfried Menninghaus$^1$ $^{1}$Department of Language and Literature, Max Planck Institute for Empirical Aesthetics $^{2}$NLLG, Department of Computer Science, T...	Yes
3a9d391d25cde8af3334ac62d478b36b30079d74	3a9d391d25cde8af3334ac62d478b36b30079d74_1	Q: Does the paper report macro F1? Text: 1.1em ::: 1.1.1em ::: ::: 1.1.1.1em Thomas Haider$^{1,3}$, Steffen Eger$^2$, Evgeny Kim$^3$, Roman Klinger$^3$, Winfried Menninghaus$^1$ $^{1}$Department of Language and Literature, Max Planck Institute for Empirical Aesthetics $^{2}$NLLG, Department of Computer Science, T...	Yes
8d8300d88283c73424c8f301ad9fdd733845eb47	8d8300d88283c73424c8f301ad9fdd733845eb47_0	Q: How is the annotation experiment evaluated? Text: 1.1em ::: 1.1.1em ::: ::: 1.1.1.1em Thomas Haider$^{1,3}$, Steffen Eger$^2$, Evgeny Kim$^3$, Roman Klinger$^3$, Winfried Menninghaus$^1$ $^{1}$Department of Language and Literature, Max Planck Institute for Empirical Aesthetics $^{2}$NLLG, Department of Compute...	confusion matrices of labels between annotators
48b12eb53e2d507343f19b8a667696a39b719807	48b12eb53e2d507343f19b8a667696a39b719807_0	Q: What are the aesthetic emotions formalized? Text: 1.1em ::: 1.1.1em ::: ::: 1.1.1.1em Thomas Haider$^{1,3}$, Steffen Eger$^2$, Evgeny Kim$^3$, Roman Klinger$^3$, Winfried Menninghaus$^1$ $^{1}$Department of Language and Literature, Max Planck Institute for Empirical Aesthetics $^{2}$NLLG, Department of Compute...	feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking), Emotions that exhibit this dual capacity have been defined as “aesthetic emotions”
003f884d3893532f8c302431c9f70be6f64d9be8	003f884d3893532f8c302431c9f70be6f64d9be8_0	Q: Do they report results only on English data? Text: Introduction “If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.” — Italo Calvino, Invisible Cities A community's identity—defined throu...	No
003f884d3893532f8c302431c9f70be6f64d9be8	003f884d3893532f8c302431c9f70be6f64d9be8_1	Q: Do they report results only on English data? Text: Introduction “If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.” — Italo Calvino, Invisible Cities A community's identity—defined throu...	Unanswerable
bb97537a0a7c8f12a3f65eba73cefa6abcd2f2b2	bb97537a0a7c8f12a3f65eba73cefa6abcd2f2b2_0	Q: How do the various social phenomena examined manifest in different types of communities? Text: Introduction “If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.” — Italo Calvino, Invisible...	Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average number o...
eea089baedc0ce80731c8fdcb064b82f584f483a	eea089baedc0ce80731c8fdcb064b82f584f483a_0	Q: What patterns do they observe about how user engagement varies with the characteristics of a community? Text: Introduction “If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.” — Italo Cal...	communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members, within distinctive communities, established users have an increased propensity to engage with the community's special...
edb2d24d6d10af13931b3a47a6543bd469752f0c	edb2d24d6d10af13931b3a47a6543bd469752f0c_0	Q: How did the select the 300 Reddit communities for comparison? Text: Introduction “If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.” — Italo Calvino, Invisible Cities A community's ident...	They selected all the subreddits from January 2013 to December 2014 with at least 500 words in the vocabulary and at least 4 months of the subreddit's history. They also removed communities with the bulk of the contributions are in foreign language.
edb2d24d6d10af13931b3a47a6543bd469752f0c	edb2d24d6d10af13931b3a47a6543bd469752f0c_1	Q: How did the select the 300 Reddit communities for comparison? Text: Introduction “If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.” — Italo Calvino, Invisible Cities A community's ident...	They collect subreddits from January 2013 to December 2014,2 for which there are at least 500 words in the vocabulary used to estimate the measures, in at least 4 months of the subreddit’s history. They compute our measures over the comments written by users in a community in time windows of months, for each sufficient...
938cf30c4f1d14fa182e82919e16072fdbcf2a82	938cf30c4f1d14fa182e82919e16072fdbcf2a82_0	Q: How do the authors measure how temporally dynamic a community is? Text: Introduction “If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.” — Italo Calvino, Invisible Cities A community's i...	the average volatility of all utterances
93f4ad6568207c9bd10d712a52f8de25b3ebadd4	93f4ad6568207c9bd10d712a52f8de25b3ebadd4_0	Q: How do the authors measure how distinctive a community is? Text: Introduction “If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.” — Italo Calvino, Invisible Cities A community's identity...	the average specificity of all utterances
71a7153e12879defa186bfb6dbafe79c74265e10	71a7153e12879defa186bfb6dbafe79c74265e10_0	Q: What data is the language model pretrained on? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is,...	Chinese general corpus
71a7153e12879defa186bfb6dbafe79c74265e10	71a7153e12879defa186bfb6dbafe79c74265e10_1	Q: What data is the language model pretrained on? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is,...	Unanswerable
85d1831c28d3c19c84472589a252e28e9884500f	85d1831c28d3c19c84472589a252e28e9884500f_0	Q: What baselines is the proposed model compared against? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor ...	BERT-Base, QANet
85d1831c28d3c19c84472589a252e28e9884500f	85d1831c28d3c19c84472589a252e28e9884500f_1	Q: What baselines is the proposed model compared against? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor ...	QANet BIBREF39, BERT-Base BIBREF26
1959e0ebc21fafdf1dd20c6ea054161ba7446f61	1959e0ebc21fafdf1dd20c6ea054161ba7446f61_0	Q: How is the clinical text structuring task defined? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size...	Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the spe...
1959e0ebc21fafdf1dd20c6ea054161ba7446f61	1959e0ebc21fafdf1dd20c6ea054161ba7446f61_1	Q: How is the clinical text structuring task defined? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size...	CTS is extracting structural data from medical research data (unstructured). Authors define QA-CTS task that aims to discover most related text from original text.
77cf4379106463b6ebcb5eb8fa5bb25450fa5fb8	77cf4379106463b6ebcb5eb8fa5bb25450fa5fb8_0	Q: What are the specific tasks being unified? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how...	three types of questions, namely tumor size, proximal resection margin and distal resection margin
77cf4379106463b6ebcb5eb8fa5bb25450fa5fb8	77cf4379106463b6ebcb5eb8fa5bb25450fa5fb8_1	Q: What are the specific tasks being unified? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how...	Unanswerable
06095a4dee77e9a570837b35fc38e77228664f91	06095a4dee77e9a570837b35fc38e77228664f91_0	Q: Is all text in this dataset a question, or are there unrelated sentences in between questions? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specifi...	the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences
19c9cfbc4f29104200393e848b7b9be41913a7ac	19c9cfbc4f29104200393e848b7b9be41913a7ac_0	Q: How many questions are in the dataset? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far...	2,714
6743c1dd7764fc652cfe2ea29097ea09b5544bc3	6743c1dd7764fc652cfe2ea29097ea09b5544bc3_0	Q: What is the perWhat are the tasks evaluated? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, h...	Unanswerable
14323046220b2aea8f15fba86819cbccc389ed8b	14323046220b2aea8f15fba86819cbccc389ed8b_0	Q: Are there privacy concerns with clinical data? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is,...	Unanswerable
08a5f8d36298b57f6a4fcb4b6ae5796dc5d944a4	08a5f8d36298b57f6a4fcb4b6ae5796dc5d944a4_0	Q: How they introduce domain-specific features into pre-trained language model? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseas...	integrate clinical named entity information into pre-trained language model
975a4ac9773a4af551142c324b64a0858670d06e	975a4ac9773a4af551142c324b64a0858670d06e_0	Q: How big is QA-CTS task dataset? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from t...	17,833 sentences, 826,987 characters and 2,714 question-answer pairs
326e08a0f5753b90622902bd4a9c94849a24b773	326e08a0f5753b90622902bd4a9c94849a24b773_0	Q: How big is dataset of pathology reports collected from Ruijing Hospital? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, ...	17,833 sentences, 826,987 characters and 2,714 question-answer pairs
bd78483a746fda4805a7678286f82d9621bc45cf	bd78483a746fda4805a7678286f82d9621bc45cf_0	Q: What are strong baseline models in specific tasks? Text: Introduction Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size...	state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26