paper_id stringlengths 10 10 | title stringlengths 34 102 | abstract stringlengths 281 1.57k | paragraphs listlengths 24 526 | question stringlengths 21 103 | evidence listlengths 1 13 |
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1909.00694 | Minimally Supervised Learning of Affective Events Using Discourse Relations | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | [
"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 important to various natural lan... | What is the seed lexicon? | [
"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 ... |
1909.00694 | Minimally Supervised Learning of Affective Events Using Discourse Relations | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | [
"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 important to various natural lan... | What are the results? | [
"Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in the seed lexicon. We can see that the see... |
1909.00694 | Minimally Supervised Learning of Affective Events Using Discourse Relations | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | [
"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 important to various natural lan... | How are relations used to propagate polarity? | [
"In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that di... |
1909.00694 | Minimally Supervised Learning of Affective Events Using Discourse Relations | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | [
"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 important to various natural lan... | How big is the Japanese data? | [
"As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into wh... |
1909.00694 | Minimally Supervised Learning of Affective Events Using Discourse Relations | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | [
"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 important to various natural lan... | What are labels available in dataset for supervision? | [
"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 important to various natural lan... |
1909.00694 | Minimally Supervised Learning of Affective Events Using Discourse Relations | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | [
"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 important to various natural lan... | How does their model learn using mostly raw data? | [
"In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that di... |
1909.00694 | Minimally Supervised Learning of Affective Events Using Discourse Relations | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | [
"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 important to various natural lan... | How big is seed lexicon used for training? | [
"We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that posit... |
1909.00694 | Minimally Supervised Learning of Affective Events Using Discourse Relations | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | [
"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 important to various natural lan... | How large is raw corpus used for training? | [
"As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into wh... |
2003.07723 | PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry | Most approaches to emotion analysis regarding social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions that have been shown to also include ... | [
"1.1em",
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"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, Technische ... | Does the paper report macro F1? | [
"We split the randomized German dataset so that each label is at least 10 times in the validation set (63 instances, 113 labels), and at least 10 times in the test set (56 instances, 108 labels) and leave the rest for training (617 instances, 946 labels). We train BERT for 10 epochs (with a batch size of 8), optimi... |
2003.07723 | "PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poe(...TRUNCATED) | "Most approaches to emotion analysis regarding social media, literature, news, and other domains foc(...TRUNCATED) | ["1.1em"," ::: : 1.1.1em"," ::: ::: : 1.1.1.1em","Thomas Haider$^{1,3}$, Steffen Eger$^2$, Evgeny K(...TRUNCATED) | How is the annotation experiment evaluated? | ["Expert Annotation ::: Agreement: Table TABREF20 shows the Cohen's $\\kappa $ agreement scores amon(...TRUNCATED) |
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