14 citations · 33 across the 9 of their papers we have counts for
9 papers
Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data
Olia Toporkov, Alan Akbik, Rodrigo Agerri
Lemmatization is the task of transforming all words in a given text to their dictionary forms. While large language models (LLMs) have demonstrated their ability to achieve competi…
Evaluating Design Decisions for Dual Encoder-based Entity Disambiguation
Susanna Rücker, Alan Akbik
Entity disambiguation (ED) is the task of linking mentions in text to corresponding entries in a knowledge base. Dual Encoders address this by embedding mentions and label candidat…
CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset
Susanna Rücker, Alan Akbik
The CoNLL-03 corpus is arguably the most well-known and utilized benchmark dataset for named entity recognition (NER). However, prior works found significant numbers of annotation…
OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs
Patrick Haller, Ansar Aynetdinov, Alan Akbik
Instruction-tuned Large Language Models (LLMs) have recently showcased remarkable ability to generate fitting responses to natural language instructions. However, an open research…
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Jonas Golde, Patrick Haller, Felix Hamborg +2
Most NLP tasks are modeled as supervised learning and thus require labeled training data to train effective models. However, manually producing such data at sufficient quality and…
Task-Specific Embeddings for Ante-Hoc Explainable Text Classification
Kishaloy Halder, Josip Krapac, Alan Akbik +2
Current state-of-the-art approaches to text classification typically leverage BERT-style Transformer models with a softmax classifier, jointly fine-tuned to predict class labels of…
Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning
Angelo Ziletti, Alan Akbik, Christoph Berns +3
Medical coding (MC) is an essential pre-requisite for reliable data retrieval and reporting. Given a free-text reported term (RT) such as "pain of right thigh to the knee", the tas…
FLERT: Document-Level Features for Named Entity Recognition
Stefan Schweter, Alan Akbik
Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level and thus do not model information that crosses sentence boundar…
Syntax-Aware Language Modeling with Recurrent Neural Networks
Duncan Blythe, Alan Akbik, Roland Vollgraf
Neural language models (LMs) are typically trained using only lexical features, such as surface forms of words. In this paper, we argue this deprives the LM of crucial syntactic si…