Papers Task 2
“Task 2” 태그가 달린 논문 572편 · 필터 해제
Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018
The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning syste…
ClassificationGeneral ClassificationTask 2Text ClassificationCopenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding
This paper documents the Team Copenhagen system which placed first in the CoNLL--SIGMORPHON 2018 shared task on universal morphological reinflection, Task 2 with an overall accuracy of 49.87. Task 2 focuses on morphologi…
DecoderLEMMAMorphological InflectionMulti-Task Learning+1Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
Clinical Named Entity Recognition (CNER) aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task fo…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Task 2+1DIDEC: The Dutch Image Description and Eye-tracking Corpus
We present a corpus of spoken Dutch image descriptions, paired with two sets of eye-tracking data: Free viewing, where participants look at images without any particular purpose, and Description viewing, where we track e…
Image DescriptionSpecificityTask 2REFUGE CHALLENGE 2018-Task 2:Deep Optic Disc and Cup Segmentation in Fundus Images Using U-Net and Multi-scale Feature Matching Networks
In this paper, an optic disc and cup segmentation method is proposed using U-Net followed by a multi-scale feature matching network. The proposed method targets task 2 of the REFUGE challenge 2018. In order to solve the …
DecoderSegmentationTask 2General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platform as "Freesound General-Purpose Audio T…
Audio TaggingTask 2Lesion Analysis and Diagnosis with Mask-RCNN
This project applies Mask R-CNN method to ISIC 2018 challenge tasks: lesion boundary segmentation (task1), lesion attributes detection (task 2), lesion diagnosis (task 3), a solution to the latter is using a trained mode…
SegmentationTask 2One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video
Due to burdensome data requirements, learning from demonstration often falls short of its promise to allow users to quickly and naturally program robots. Demonstrations are inherently ambiguous and incomplete, making cor…
One-Shot LearningReinforcement LearningTask 2SemEval 2018 Task 2: Multilingual Emoji Prediction
This paper describes the results of the first Shared Task on Multilingual Emoji Prediction, organized as part of SemEval 2018. Given the text of a tweet, the task consists of predicting the most likely emoji to be used a…
Emotion RecognitionInformation RetrievalPredictionSentiment Analysis+1T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction
This paper describes our participation in the SemEval-2018 task Multilingual Emoji Prediction. We participated in both English and Spanish subtasks, experimenting with support vector machines (SVMs) and recurrent neural …
Document ClassificationGeneral ClassificationHyperparameter OptimizationLanguage Identification+4EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM
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 Bidir…
BIG-bench Machine LearningFeature EngineeringregressionTask 2UMDuluth-CS8761 at SemEval-2018 Task 2: Emojis: Too many Choices?
In this paper, we present our system for assigning an emoji to a tweet based on the text. Each tweet was originally posted with an emoji which the task providers removed. Our task was to decide out of 20 emojis, which or…
Task 2The Dabblers at SemEval-2018 Task 2: Multilingual Emoji Prediction
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, w…
BIG-bench Machine LearningRelationTask 2THU\_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction
Emojis are widely used by social media and social network users when posting their messages. It is important to study the relationships between messages and emojis. Thus, in SemEval-2018 Task 2 an interesting and challen…
POSSentiment AnalysisTask 2\#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets
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.…
General ClassificationInformation RetrievalregressionSentiment Analysis+2EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction
The advent of social media has brought along a novel way of communication where meaning is composed by combining short text messages and visual enhancements, the so-called emojis. We describe our system for participating…
Information RetrievalPredictionSentiment AnalysisTask 2+1EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words
This paper presents our single model to Subtask 1 of SemEval 2018 Task 2: Emoji Prediction in English. In order to predict the emoji that may be contained in a tweet, the basic model we use is an attention-based recurren…
Task 2Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction
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-c…
ClassificationGeneral ClassificationSemantic Textual SimilaritySentiment Analysis+3ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction Task
This paper describes our submissions to Task 2 in SemEval 2018, i.e., Multilingual Emoji Prediction. We first investigate several traditional Natural Language Processing (NLP) features, and then design several deep learn…
Deep LearningPredictionTask 2Hatching Chick at SemEval-2018 Task 2: Multilingual Emoji Prediction
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 we…
Task 2