[PDF] Top 20 LSTMs with Attention for Aggression Detection
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LSTMs with Attention for Aggression Detection
... an attention module. The attention module helps the model determine which word to give more importance ...from attention module is passed through a fully-connected ... See full document
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AELA-DLSTMs: Attention-enabled and location-aware double LSTMs for aspect-level sentiment classification
... Double LSTMs) and AELA-DLSTMs (Attention-Enabled and Location-Aware Double LSTMs) are proposed for aspect-level sentiment ...(Double LSTMs) which can capture the contextual semantic ... See full document
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Incremental Fine grained Information Status Classification Using Attention based LSTMs
... In this paper, we focus on classifying IS on written text because many applications which can benefit from IS concentrate on written texts. We follow the IS scheme for written text proposed by Markert et al. (2012). It ... See full document
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Classification of Medication Related Tweets Using Stacked Bidirectional LSTMs with Context Aware Attention
... model is a word-level stacked bidirectional LSTM (BiLSTM) with context-aware attention that uses word-embeddings pretrained by (Baziotis et al., 2017) on a corpus of ≈ 330M tweets. Without ad- ditional ... See full document
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Interpretable Emoji Prediction via Label Wise Attention LSTMs
... proposed attention mech- anism, in comparison with existing emoji predic- tion ...wise attention mechanism in context, we com- pare its performance with a set of baselines: (1) FastText (Joulin et ... See full document
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Transition Based Disfluency Detection using LSTMs
... score as shown in Table 5. It achieves 2.4 point im- provements over UBT (Wu et al., 2015), which is the best syntax-based method for disfluency detec- tion. The best performance by linear statistical se- quence labeling ... See full document
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ANA at SemEval 2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
... more attention (Yad- dolahi et ...automatic detection may reveal important information in so- cial online environments, like online customer ser- ... See full document
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Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
... Monday, August 8, 2016 continued Text Understanding with the Attention Sum Reader Network Rudolf Kadlec, Martin Schmid, Ondˇrej Bajgar and Jan Kleindienst Investigating LSTMs for Joint E[r] ... See full document
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Supertagging With LSTMs
... Morphosyntactic labels for words are commonly used in a variety of NLP applications. For this rea- son, part-of-speech (POS) tagging and supertagging have drawn significant attention from the commu- nity. ... See full document
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Citation Analysis with Neural Attention Models
... bi-directional LSTMs with CAN is the clear winner in terms of the test perfor- ...global attention models decreased in response to additional context given in the ... See full document
9
Deep Attentive Sentence Ordering Network
... As shown in Table 3, ATTOrderNet performs much better than data-driven methods by a sig- nificant margin on all corresponding datasets. It proves the importance of exploiting the context by self-attention ... See full document
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Sentence Compression by Deletion with LSTMs
... In this paper we research the following ques- tion: can a robust compression model be built which only uses tokens and has no access to syn- tactic or other linguistic information? While phe- nomena like long-distance ... See full document
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A Neural Attention Model for Disfluency Detection
... Figure 1: A sentence with disfluencies annotated in the style of (Shriberg, 1994) and the Switchboard corpus. FP=Filled Pause, RM=Reparandum, IM=Interregnum, RP=Repair. We follow previous works in evaluating the system ... See full document
10
A K Competitive Autoencoder for Aggression Detection in Social Media Text
... automatic detection of aggressive text is the logical first step to combat the ...detecting aggression on social media posts or comments is especially challenging due to its lack of ... See full document
10
Self-Attention Networks for Intent Detection
... with attention mechanism (Xu, et ...intent detection and other SLU tasks (Liu and Lane, 2016; Schumann and Angkititrakul, ...intent detection and slot ... See full document
7
Generating Steganographic Text with LSTMs
... 2.2 Our Proposal: Steganographic LSTM Motivated by the fact that LSTMs (Hochreiter and Schmidhuber, 1997) constitute the state of the art in text generation (Jozefowicz et al., 2016), we propose to automatically ... See full document
7
Cubic LSTMs for Video Prediction
... with shape (height, width, channel). As FC-LSTMs are de- signed to learn only one type of information, directly adapt- ing FC-LSTM makes it difficult for ConvLSTM to simul- taneously process the temporal ... See full document
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STUDY ON DIFFERENT CORRELATIONS BETWEEN MEASURES OF TEMPERAMENT AND THOSE OF AGGRESSION
... classifying aggression becomes more difficult because there is complication of ...predatory aggression, which refers to stalking and killing of other species, (2) social aggression, which is ... See full document
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The effect of faradarmani lifestyle on aggression in female players of iranian basketball league
... earlier, aggression can influence athletes' performance, which consequently impacts on the results of the competition, and Faradarmani lifestyle is of high importance in preventing and treating mental and physical ... See full document
5
Fermi at SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings
... Using computational methods to identify of- fense, aggression and hate speech in user gener- ated content has been gaining attention in the re- cent years as evidenced in (Waseem et al., 2017; Davidson et ... See full document
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