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Found 39 papers, 11 papers with code
SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi
Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
WueDevils at SemEval-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices
We present a system that creates pair-wise cosine and arccosine sentence similarity matrices using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder (USE) models respectively.
LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance
This paper introduces our submission for the SemEval 2022 Task 8: Multilingual News Article Similarity.
GrINd: Grid Interpolation Network for Scattered Observations
GrINd offers a promising approach for forecasting physical systems from sparse, scattered observational data, extending the applicability of deep learning methods to real-world scenarios with limited data availability.
Global Vegetation Modeling with Pre-Trained Weather Transformers
We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch.
BibSonomy Meets ChatLLMs for Publication Management: From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs
The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications.
Higher-Order DeepTrails: Unified Approach to *Trails
Analyzing, understanding, and describing human behavior is advantageous in different settings, such as web browsing or traffic navigation.
TaylorPDENet: Learning PDEs from non-grid Data
Modeling data obtained from dynamical systems has gained attention in recent years as a challenging task for machine learning models.
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data
The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements.
Towards a Computational Analysis of Suspense: Detecting Dangerous Situations
Suspense is an important tool in storytelling to keep readers engaged and wanting to read more.
Versatile User Identification in Extended Reality using Pretrained Similarity-Learning
Furthermore, we extended this evaluation using an independent dataset that features completely different users, tasks, and three different XR devices.
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images
Therefore, we present Language-Guided Zero-Shot Deep Metric Learning (LanZ-DML) as a new DML setting in which users control the properties that should be important for image representations without training data by only using natural language.
One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium
Natural Language Processing and Machine Learning have considerably advanced Computational Literary Studies.
On Background Bias in Deep Metric Learning
Deep Metric Learning trains a neural network to map input images to a lower-dimensional embedding space such that similar images are closer together than dissimilar images.
Comparison of Data Representations and Machine Learning Architectures for User Identification on Arbitrary Motion Sequences
Reliable and robust user identification and authentication are important and often necessary requirements for many digital services.
Semi-unsupervised Learning for Time Series Classification
Due to its chaotic nature and massive amounts of datapoints, timeseries are hard to label manually.
Open ERP System Data For Occupational Fraud Detection
Recent estimates report that companies lose 5% of their revenue to occupational fraud.
Do Different Deep Metric Learning Losses Lead to Similar Learned Features?
In this paper, we investigate this by conducting a two-step analysis to extract and compare the learned visual features of the same model architecture trained with different loss functions: First, we compare the learned features on the pixel level by correlating saliency maps of the same input images.
Sequential Item Recommendation in the MOBA Game Dota 2
We find that SIR models can be employed effectively for item recommendation in Dota 2.
NeuralPDE: Modelling Dynamical Systems from Data
Many physical processes such as weather phenomena or fluid mechanics are governed by partial differential equations (PDEs).
Anomaly Detection in Beehives: An Algorithm Comparison
Sensor-equipped beehives allow monitoring the living conditions of bees.
A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models
Then we evaluate all models on a target set sampled by the two different sampling strategies, uniform random sampling and sampling by popularity with the commonly used target set size of 100, compute the model ranking for each strategy and compare them with each other.
Detecting Scenes in Fiction: A new Segmentation Task
This paper introduces the novel task of scene segmentation on narrative texts and provides an annotated corpus, a discussion of the linguistic and narrative properties of the task and baseline experiments towards automatic solutions.
Deep Learning for Climate Model Output Statistics
Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation.
NICER: Aesthetic Image Enhancement with Humans in the Loop
However, fully-automatic approaches usually enhance the image in a black-box manner that does not give the user any control over the optimization process, possibly leading to edited images that do not subjectively appeal to the user.
Where to Submit? Helping Researchers to Choose the Right Venue
Whenever researchers write a paper, the same question occurs: {``}Where to submit?
Improving Sentiment Analysis with Biofeedback Data
Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories.
iNALU: Improved Neural Arithmetic Logic Unit
Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.
Anomaly Detection in Beehives using Deep Recurrent Autoencoders
Precision beekeeping allows to monitor bees' living conditions by equipping beehives with sensors.
SimLoss: Class Similarities in Cross Entropy
One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally.
MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images
In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data.
Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks
This paper describes our system for the SemEval 2019 Task 4 on hyperpartisan news detection.
Flow-based Network Traffic Generation using Generative Adversarial Networks
We use the three approaches for generating flow-based network traffic based on the CIDDS-001 data set.
ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings
In this paper we describe our system for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1. 1) and noisy data (subtask 1. 2).
ClaiRE at SemEval-2018 Task 7 - Extended Version
Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75. 11% for the first subtask and 81. 44% for the second.
Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data
This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized.
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time
Recently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation.
Learning Semantic Relatedness From Human Feedback Using Metric Learning
To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback.
Analyzing Features for the Detection of Happy Endings in German Novels
With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels.
