Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Ghaffari, Parsa"'
We present Simplified Text-Attributed Graph Embeddings (STAGE), a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our approach leverages Large-Language M
Externí odkaz:
http://arxiv.org/abs/2407.12860
Autor:
Boylan, Jack, Mangla, Shashank, Thorn, Dominic, Ghalandari, Demian Gholipour, Ghaffari, Parsa, Hokamp, Chris
This study explores the use of Large Language Models (LLMs) for automatic evaluation of knowledge graph (KG) completion models. Historically, validating information in KGs has been a challenging task, requiring large-scale human annotation at prohibi
Externí odkaz:
http://arxiv.org/abs/2404.15923
We present an open-source Python library for building and using datasets where inputs are clusters of textual data, and outputs are sequences of real values representing one or more time series signals. The news-signals library supports diverse data
Externí odkaz:
http://arxiv.org/abs/2312.11399
The proliferation of fake news and filter bubbles makes it increasingly difficult to form an unbiased, balanced opinion towards a topic. To ameliorate this, we propose 360{\deg} Stance Detection, a tool that aggregates news with multiple perspectives
Externí odkaz:
http://arxiv.org/abs/1804.00982
Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing approaches train on all available source domains including dissimilar o
Externí odkaz:
http://arxiv.org/abs/1702.02426
Domain adaptation is crucial in many real-world applications where the distribution of the training data differs from the distribution of the test data. Previous Deep Learning-based approaches to domain adaptation need to be trained jointly on source
Externí odkaz:
http://arxiv.org/abs/1702.02052
Humans continuously adapt their style and language to a variety of domains. However, a reliable definition of `domain' has eluded researchers thus far. Additionally, the notion of discrete domains stands in contrast to the multiplicity of heterogeneo
Externí odkaz:
http://arxiv.org/abs/1610.09158
Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision. Recently, character-level and multi-channel CNNs have exhibited excellent performance for sentence classificati
Externí odkaz:
http://arxiv.org/abs/1609.06686
Publikováno v:
Proceedings of SemEval (2016): 178-182
This paper describes our deep learning-based approach to sentiment analysis in Twitter as part of SemEval-2016 Task 4. We use a convolutional neural network to determine sentiment and participate in all subtasks, i.e. two-point, three-point, and five
Externí odkaz:
http://arxiv.org/abs/1609.02746
Opinion mining from customer reviews has become pervasive in recent years. Sentences in reviews, however, are usually classified independently, even though they form part of a review's argumentative structure. Intuitively, sentences in a review build
Externí odkaz:
http://arxiv.org/abs/1609.02745