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 |
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