no code implementations • 18 Dec 2023 • Chris Hokamp, Demian Gholipour Ghalandari, Parsa Ghaffari
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.
no code implementations • NAACL 2018 • Sebastian Ruder, John Glover, Afshin Mehrabani, Parsa Ghaffari
To ameliorate this, we propose 360{\mbox{$^\circ$}} Stance Detection, a tool that aggregates news with multiple perspectives on a topic.
no code implementations • 3 Apr 2018 • Sebastian Ruder, John Glover, Afshin Mehrabani, Parsa Ghaffari
To ameliorate this, we propose 360{\deg} Stance Detection, a tool that aggregates news with multiple perspectives on a topic.
1 code implementation • 8 Feb 2017 • Sebastian Ruder, Parsa Ghaffari, John G. Breslin
However, the selection of appropriate training data is as important as the choice of algorithm.
no code implementations • 7 Feb 2017 • Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Domain adaptation is crucial in many real-world applications where the distribution of the training data differs from the distribution of the test data.
no code implementations • WS 2016 • Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Humans continuously adapt their style and language to a variety of domains.
3 code implementations • 21 Sep 2016 • Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision.
no code implementations • EMNLP 2016 • Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Opinion mining from customer reviews has become pervasive in recent years.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3
no code implementations • SEMEVAL 2016 • Sebastian Ruder, Parsa Ghaffari, John G. Breslin
This paper describes our deep learning-based approach to sentiment analysis in Twitter as part of SemEval-2016 Task 4.
no code implementations • SEMEVAL 2016 • Sebastian Ruder, Parsa Ghaffari, John G. Breslin
This paper describes our deep learning-based approach to multilingual aspect-based sentiment analysis as part of SemEval 2016 Task 5.
Aspect-Based Sentiment Analysis Aspect Category Detection +3