62 citations · 274 across the 31 of their papers we have counts for
14 papers · 1 filter
Exploring the Landscape of Natural Language Processing Research
Tim Schopf, Karim Arabi, Florian Matthes
As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption i…
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge
Tim Schopf, Emanuel Gerber, Malte Ostendorff +1
Generic sentence embeddings provide a coarse-grained approximation of semantic textual similarity but ignore specific aspects that make texts similar. Conversely, aspect-based sent…
Efficient Domain Adaptation of Sentence Embeddings Using Adapters
Tim Schopf, Dennis N. Schneider, Florian Matthes
Sentence embeddings enable us to capture the semantic similarity of short texts. Most sentence embedding models are trained for general semantic textual similarity tasks. Therefore…
Scientific Fact-Checking: A Survey of Resources and Approaches
Juraj Vladika, Florian Matthes
The task of fact-checking deals with assessing the veracity of factual claims based on credible evidence and background knowledge. In particular, scientific fact-checking is the va…
Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports
Juraj Vladika, Florian Matthes
With the increasing number of clinical trial reports generated every day, it is becoming hard to keep up with novel discoveries that inform evidence-based healthcare recommendation…
Voice-Based Conversational Agents and Knowledge Graphs for Improving News Search in Assisted Living
Phillip Schneider, Nils Rehtanz, Kristiina Jokinen +1
As the healthcare sector is facing major challenges, such as aging populations, staff shortages, and common chronic diseases, delivering high-quality care to individuals has become…
Investigating Conversational Search Behavior For Domain Exploration
Phillip Schneider, Anum Afzal, Juraj Vladika +2
Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search age…
Semantic Similarity-Based Clustering of Findings From Security Testing Tools
Phillip Schneider, Markus Voggenreiter, Abdullah Gulraiz +1
Over the last years, software development in domains with high security demands transitioned from traditional methodologies to uniting modern approaches from software development a…
Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches
Tim Schopf, Daniel Braun, Florian Matthes
Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches…
Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics
Tim Schopf, Daniel Braun, Florian Matthes
In this paper, we consider the task of retrieving documents with predefined topics from an unlabeled document dataset using an unsupervised approach. The proposed unsupervised appr…
PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction
Tim Schopf, Simon Klimek, Florian Matthes
Keyphrase extraction is the process of automatically selecting a small set of most relevant phrases from a given text. Supervised keyphrase extraction approaches need large amounts…
A Decade of Knowledge Graphs in Natural Language Processing: A Survey
Phillip Schneider, Tim Schopf, Juraj Vladika +3
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a represen…
Differential Privacy in Natural Language Processing: The Story So Far
Oleksandra Klymenko, Stephen Meisenbacher, Florian Matthes
As the tide of Big Data continues to influence the landscape of Natural Language Processing (NLP), the utilization of modern NLP methods has grounded itself in this data, in order…
Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification
Ahmed Elnaggar, Christoph Gebendorfer, Ingo Glaser +1
The digitalization of the legal domain has been ongoing for a couple of years. In that process, the application of different machine learning (ML) techniques is crucial. Tasks such…