103 citations · 123 across the 4 of their papers we have counts for
7 papers
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
Phillip Howard, Arden Ma, Vasudev Lal +5
The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training an…
Efficient Few-Shot Learning Without Prompts
Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo +4
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting training (PET), have achieved impressive results in label-scarce settings. However, t…
TangoBERT: Reducing Inference Cost by using Cascaded Architecture
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +1
The remarkable success of large transformer-based models such as BERT, RoBERTa and XLNet in many NLP tasks comes with a large increase in monetary and environmental cost due to the…
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System
Oren Pereg, Daniel Korat, Moshe Wasserblat +2
We present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction. The system is interpretable and user friendly and does not require labeled training da…
Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +1
In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set…
Term Set Expansion based NLP Architect by Intel AI Lab
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +5
We present SetExpander, a corpus-based system for expanding a seed set of terms into amore complete set of terms that belong to the same semantic class. SetExpander implements an i…