[PDF] Top 20 DMGroup at EmoInt 2017: Emotion Intensity Using Ensemble Method
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DMGroup at EmoInt 2017: Emotion Intensity Using Ensemble Method
... analyzing emotion have been ...work using deeply models (Majumder et al, ...trained using bag-of- words features produced hopeful ...used emotion icons in the blog posts as significant ... See full document
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NSEmo at EmoInt 2017: An Ensemble to Predict Emotion Intensity in Tweets
... an ensemble method to implement a collaborative filtering algorithm (T¨oscher et ...an ensemble method (Niculescu- Mizil et ...based method for polarity classifica- tion in ... See full document
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Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets
... Network Architecture: As shown in figure 1, the concatenated vector representation of the tweet is first fed to a LSTM or CNN and then some fully connected (dense) hidden layers. The representa- tion learned in the last ... See full document
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UWat Emote at EmoInt 2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings
... for EmoInt- ...affect intensity, af- fect presence, sentiment intensity and sentiment presence lexica alongside pre- trained word embeddings, which are uti- lized to extract emotion ... See full document
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Textmining at EmoInt 2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets
... We have applied a unified deep learning model to the emotion intensity task on twitter data. Two sets of features have been extracted using traditional NLP methods and recent deep learning based fea- ... See full document
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GradAscent at EmoInt 2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection
... WASSA 2017 EmoInt shared task has the goal to predict emotion intensity val- ues of tweet ...its emotion category (anger, joy, fear, and sadness), the participants were asked to build a ... See full document
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YNU HPCC at EmoInt 2017: Using a CNN LSTM Model for Sentiment Intensity Prediction
... In the relevant research field of sentiment analy- sis, it has been shown that many models are avail- able for both categorical approaches and dimen- sional approaches. A categorical approach focus- es on sentiment ... See full document
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IMS at EmoInt 2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
... Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms. We combine three main ... See full document
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NUIG at EmoInt 2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity
... the Emotion Hashtag Corpus (Mohammad, 2012) as training data for the BiLSTM ...for intensity of the emotion ...as emotion labels and that emotion probability is not a good analogue for ... See full document
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YZU NLP at EmoInt 2017: Determining Emotion Intensity Using a Bi directional LSTM CNN Model
... The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is ex- pressed in a ...proposed method ranked sixth among twenty-one ... See full document
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An Ensemble of Classifiers using Dynamic Method on Ambiguous Data
... of ensemble of classifiers is to combine all classifiers and learn by reduced training ...an ensemble such as Bagging (Bootstrap Aggregating), Boosting (ADABOOST) and Error Correcting Output Code (ECOC) ... See full document
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Confidence Measures in Multiclass Speech Emotion Recognition Using Ensemble Learning To Catch Blunders
... Finally it can be seen in Table 3 above that each of the methods of fusion specifically the mean, median and Bayesian methods have a high confidence accuracy, that is to say any time confidence is assigned to a ... See full document
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Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories
... The categorizations usually found in lexicons (e.g. Linguistic Inquiry and Word Count or LIWC) treat words as stimuli which humans re- spond to with emotions. This model aligns closely with basic emotions, and supposes ... See full document
5
Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts
... the map shown in Fig. 3) to give a single value for the to- tal severity of the storm, which can then be compared with the reforecast. This method is equivalent to the SSI defined by Leckebusch et al. (2007). It ... See full document
16
Speech Emotion Recognition based on Voiced Emotion Unit
... proposed method for segmentation of speech utterance into its emotion ...an emotion unit should be investigated within the voiced ...or using an ASR system to determine these ...done ... See full document
7
Feature Enriched Character Level Convolutions for Text Regression
... We present a new model for text re- gression that seamlessly combine engi- neered features and character-level infor- mation through deep parallel convolution stacks, multi-layer perceptrons and multi- task learning. We ... See full document
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Learning representations for sentiment classification using Multi task framework
... developed using wordvectors along with lexicons and support vec- tor as final ...al., 2017) and Siamese network (Baziotis et al., 2017), which were top performing teams in the ... See full document
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Probabilistic forecasting of wind power production losses in cold climates: a case study
... the ensemble prediction system HarmonEPS is ...al., 2017), also as an ensemble in HarmonEPS since ...HarmonEPS ensemble consists of 10 perturbed members and 1 control ...of ensemble ... See full document
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Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method
... geolocation. Ensemble learning methods are also widely developed for clinical ...hybrid ensemble algorithm for cervical cancer ...and ensemble by a MLP classifier in their study of hepatitis C ... See full document
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An Ensemble Method for Character Recognition Using Machine Learning Techniques
... The ensemble system uses Breiman’s bagging, short for bootstrap aggregating,[1] the earliest ensemble based ...by using bootstrapped replicas of the training data: different training data subsets are ... See full document
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