Paper Group NANR 269
RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning. CNNs as Inverse Problem Solvers and Double Network Superresolution. psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis. UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models. Stroke lesion detection using convolutional neural ne …
RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning
| Title | RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning |
| Authors | Venkatesh Elango, Karan Uppal |
| Abstract | We present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity of sentiment, and valence for tweets. For modeling, though we use a traditional LSTM network, we combine our model with several state-of-the-art techniques to improve its performance in a low-resource setting. For example, we use an encoder-decoder network to initialize the LSTM weights. Without any task specific optimization we achieve competitive results (macro-average Pearson correlation coefficient 0.696) in the El-reg task. In this paper, we describe our development strategy in detail along with an exposition of our results. |
| Tasks | Sentiment Analysis |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1054/ |
| https://www.aclweb.org/anthology/S18-1054 | |
| PWC | https://paperswithcode.com/paper/riddl-at-semeval-2018-task-1-rage-intensity |
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CNNs as Inverse Problem Solvers and Double Network Superresolution
| Title | CNNs as Inverse Problem Solvers and Double Network Superresolution |
| Authors | Cem TARHAN, Gözde BOZDAĞI AKAR |
| Abstract | In recent years Convolutional Neural Networks (CNN) have been used extensively for Superresolution (SR). In this paper, we use inverse problem and sparse representation solutions to form a mathematical basis for CNN operations. We show how a single neuron is able to provide the optimum solution for inverse problem, given a low resolution image dictionary as an operator. Introducing a new concept called Representation Dictionary Duality, we show that CNN elements (filters) are trained to be representation vectors and then, during reconstruction, used as dictionaries. In the light of theoretical work, we propose a new algorithm which uses two networks with different structures that are separately trained with low and high coherency image patches and show that it performs faster compared to the state-of-the-art algorithms while not sacrificing from performance. |
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| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=SyqAPeWAZ |
| https://openreview.net/pdf?id=SyqAPeWAZ | |
| PWC | https://paperswithcode.com/paper/cnns-as-inverse-problem-solvers-and-double |
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psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis
| Title | psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis |
| Authors | Grace Gee, Eugene Wang |
| Abstract | In this paper, we describe the first attempt to perform transfer learning from sentiment to emotions. Our system employs Long Short-Term Memory (LSTM) networks, including bidirectional LSTM (biLSTM) and LSTM with attention mechanism. We perform transfer learning by first pre-training the LSTM networks on sentiment data before concatenating the penultimate layers of these networks into a single vector as input to new dense layers. For the E-c subtask, we utilize a novel approach to train models for correlated emotion classes. Our system performs 4/48, 3/39, 8/38, 4/37, 4/35 on all English subtasks EI-reg, EI-oc, V-reg, V-oc, E-c of SemEval 2018 Task 1: Affect in Tweets. |
| Tasks | Emotion Classification, Emotion Recognition, Sentiment Analysis, Transfer Learning |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1056/ |
| https://www.aclweb.org/anthology/S18-1056 | |
| PWC | https://paperswithcode.com/paper/psyml-at-semeval-2018-task-1-transfer |
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UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models
| Title | UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models |
| Authors | Abhishek Avinash Narwekar, Roxana Girju |
| Abstract | Our submission to the SemEval-2018 Task1: Affect in Tweets shared task competition is a supervised learning model relying on standard lexicon features coupled with word embedding features. We used an ensemble of diverse models, including random forests, gradient boosted trees, and linear models, corrected for training-development set mismatch. We submitted the system{'}s output for subtasks 1 (emotion intensity prediction), 2 (emotion ordinal classification), 3 (valence intensity regression) and 4 (valence ordinal classification), for English tweets. We placed 25th, 19th, 24th and 15th in the four subtasks respectively. The baseline considered was an SVM (Support Vector Machines) model with linear kernel on the lexicon and embedding based features. Our system{'}s final performance measured in Pearson correlation scores outperformed the baseline by a margin of 2.2{%} to 14.6{%} across all tasks. |
| Tasks | Sentiment Analysis |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1057/ |
| https://www.aclweb.org/anthology/S18-1057 | |
| PWC | https://paperswithcode.com/paper/uiuc-at-semeval-2018-task-1-recognizing |
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Stroke lesion detection using convolutional neural networks
| Title | Stroke lesion detection using convolutional neural networks |
| Authors | Danillo Roberto Pereira, Pedro P. Rebouc¸as Filho, Gustavo Henrique de Rosa, Joao Paulo Papa, Victor Hugo C. de Albuquerque |
| Abstract | Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density’s map. The results evidenced that CNN’s are suitable to deal with stroke detection, obtaining promising results. |
| Tasks | Computed Tomography (CT), Stroke Classification |
| Published | 2018-07-08 |
| URL | https://doi.org/10.1109/IJCNN.2018.8489199 |
| https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8489199 | |
| PWC | https://paperswithcode.com/paper/stroke-lesion-detection-using-convolutional |
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KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets
| Title | KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets |
| Authors | Thomas Nyegaard-Signori, Casper Veistrup Helms, Johannes Bjerva, Isabelle Augenstein |
| Abstract | We take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in order to preserve the task relatedness and reduce complexity. We employ multi-task learning with hard parameter sharing to exploit the relatedness between sub-tasks. As a base model, we use a standard recurrent neural network for both the classification and regression subtasks. Our system ranks 32nd out of 48 participants with a Pearson score of 0.557 in the first subtask, and 20th out of 35 in the fifth subtask with an accuracy score of 0.464. |
| Tasks | Emotion Classification, Multi-Task Learning |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1058/ |
| https://www.aclweb.org/anthology/S18-1058 | |
| PWC | https://paperswithcode.com/paper/ku-mtl-at-semeval-2018-task-1-multi-task |
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EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM
| Title | EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM |
| Authors | Man Liu |
| Abstract | This paper describes our system used in the English Emoji Prediction Task 2 at the SemEval-2018. Our system is based on two supervised machine learning algorithms: Gradient Boosting Regression Tree Method (GBM) and Bidirectional Long Short-term Memory Network (BLSTM). Besides the common features, we extract various lexicon and syntactic features from external resources. After comparing the results of two algorithms, GBM is chosen for the final evaluation. |
| Tasks | Feature Engineering, Tokenization |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1059/ |
| https://www.aclweb.org/anthology/S18-1059 | |
| PWC | https://paperswithcode.com/paper/emonlp-at-semeval-2018-task-2-english-emoji |
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Egocentric Activity Recognition on a Budget
| Title | Egocentric Activity Recognition on a Budget |
| Authors | Rafael Possas, Sheila Pinto Caceres, Fabio Ramos |
| Abstract | Recent advances in embedded technology have enabled more pervasive machine learning. One of the common applications in this field is Egocentric Activity Recognition (EAR), where users wearing a device such as a smartphone or smartglasses are able to receive feedback from the embedded device. Recent research on activity recognition has mainly focused on improving accuracy by using resource intensive techniques such as multi-stream deep networks. Although this approach has provided state-of-the-art results, in most cases it neglects the natural resource constraints (e.g. battery) of wearable devices. We develop a Reinforcement Learning model-free method to learn energy-aware policies that maximize the use of low-energy cost predictors while keeping competitive accuracy levels. Our results show that a policy trained on an egocentric dataset is able use the synergy between motion sensors and vision to effectively tradeoff energy expenditure and accuracy on smartglasses operating in realistic, real-world conditions. |
| Tasks | Activity Recognition, Egocentric Activity Recognition |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Possas_Egocentric_Activity_Recognition_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Possas_Egocentric_Activity_Recognition_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/egocentric-activity-recognition-on-a-budget |
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The Dabblers at SemEval-2018 Task 2: Multilingual Emoji Prediction
| Title | The Dabblers at SemEval-2018 Task 2: Multilingual Emoji Prediction |
| Authors | Larisa Alexa, Alina Loren{\textcommabelow{t}}, Daniela G{^\i}fu, Tr, Diana ab{\u{a}}{\textcommabelow{t}} |
| Abstract | The {``}Multilingual Emoji Prediction{''} task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network). | |
| Tasks | |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1062/ |
| https://www.aclweb.org/anthology/S18-1062 | |
| PWC | https://paperswithcode.com/paper/the-dabblers-at-semeval-2018-task-2 |
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#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets
| Title | #TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets |
| Authors | Alison Ribeiro, N{'a}dia Silva |
| Abstract | In this paper, we describe a methodology to predict emoji in tweets. Our approach is based on the classic bag-of-words model in conjunction with word embeddings. The used classification algorithm was Logistic Regression. This architecture was used and evaluated in the context of the SemEval 2018 challenge (task 2, subtask 1). |
| Tasks | Information Retrieval, Sentiment Analysis, Word Embeddings |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1064/ |
| https://www.aclweb.org/anthology/S18-1064 | |
| PWC | https://paperswithcode.com/paper/teaminf-at-semeval-2018-task-2-emoji |
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Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction
| Title | Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction |
| Authors | Jing Chen, Dechuan Yang, Xilian Li, Wei Chen, Tengjiao Wang |
| Abstract | This paper describes our participation in SemEval 2018 Task 2: Multilingual Emoji Prediction, in which participants are asked to predict a tweet{'}s most associated emoji from 20 emojis. Instead of regarding it as a 20-class classification problem we regard it as a text similarity problem. We propose a vector similarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji{'}s embedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs comparably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning. |
| Tasks | Semantic Textual Similarity, Sentiment Analysis, Word Embeddings |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1067/ |
| https://www.aclweb.org/anthology/S18-1067 | |
| PWC | https://paperswithcode.com/paper/peperomia-at-semeval-2018-task-2-vector |
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Hatching Chick at SemEval-2018 Task 2: Multilingual Emoji Prediction
| Title | Hatching Chick at SemEval-2018 Task 2: Multilingual Emoji Prediction |
| Authors | Jo{"e}l Coster, Reinder Gerard van Dalen, Nathalie Adri{"e}nne Jacqueline Stierman |
| Abstract | As part of a SemEval 2018 shared task an attempt was made to build a system capable of predicting the occurence of a language{'}s most frequently used emoji in Tweets. Specifically, models for English and Spanish data were created and trained on 500.000 and 100.000 tweets respectively. In order to create these models, first a logistic regressor, a sequential LSTM, a random forest regressor and a SVM were tested. The latter was found to perform best and therefore optimized individually for both languages. During developmet f1-scores of 61 and 82 were obtained for English and Spanish data respectively, in comparison, f1-scores on the official evaluation data were 21 and 18. The significant decrease in performance during evaluation might be explained by overfitting during development and might therefore have partially be prevented by using cross-validation. Over all, emoji which occur in a very specific context such as a Christmas tree were found to be most predictable. |
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| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1070/ |
| https://www.aclweb.org/anthology/S18-1070 | |
| PWC | https://paperswithcode.com/paper/hatching-chick-at-semeval-2018-task-2 |
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DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets
| Title | DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets |
| Authors | Dimitrios Effrosynidis, Georgios Peikos, Symeon Symeonidis, Avi Arampatzis |
| Abstract | This paper describes the approach that was developed for SemEval 2018 Task 2 (Multilingual Emoji Prediction) by the DUTH Team. First, we employed a combination of pre-processing techniques to reduce the noise of tweets and produce a number of features. Then, we built several N-grams, to represent the combination of word and emojis. Finally, we trained our system with a tuned LinearSVC classifier. Our approach in the leaderboard ranked 18th amongst 48 teams. |
| Tasks | Information Retrieval |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1074/ |
| https://www.aclweb.org/anthology/S18-1074 | |
| PWC | https://paperswithcode.com/paper/duth-at-semeval-2018-task-2-emoji-prediction |
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Open Set Learning with Counterfactual Images
| Title | Open Set Learning with Counterfactual Images |
| Authors | Lawrence Neal, Matthew Olson, Xiaoli Fern, Weng-Keen Wong, Fuxin Li |
| Abstract | In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, we introduce a dataset augmentation technique that we call counterfactual image generation. Our approach, based on generative adversarial networks, generates examples that are close to training set examples yet do not belong to any training category. By augmenting training with examples generated by this optimization, we can reformulate open set recognition as classification with one additional class, which includes the set of novel and unknown examples. Our approach outperforms existing open set recognition algorithms on a selection of image classification tasks. |
| Tasks | Image Classification, Image Generation, Open Set Learning |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Lawrence_Neal_Open_Set_Learning_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Lawrence_Neal_Open_Set_Learning_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/open-set-learning-with-counterfactual-images |
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Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets
| Title | Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets |
| Authors | Luciano Gerber, Matthew Shardlow |
| Abstract | We present our submission to the Semeval 2018 task on emoji prediction. We used a random forest, with an ensemble of bag-of-words, sentiment and psycholinguistic features. Although we performed well on the trial dataset (attaining a macro f-score of 63.185 for English and 81.381 for Spanish), our approach did not perform as well on the test data. We describe our features and classi cation protocol, as well as initial experiments, concluding with a discussion of the discrepancy between our trial and test results. |
| Tasks | Text Classification |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1079/ |
| https://www.aclweb.org/anthology/S18-1079 | |
| PWC | https://paperswithcode.com/paper/manchester-metropolitan-at-semeval-2018-task |
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