62 citations · 169 across the 17 of their papers we have counts for
17 papers
Think-Probe-Respond: Improving Large Language Models as Judges of Research Idea Novelty
Tim Schopf, Tobias Schreieder, Akiko Aizawa
Automated novelty judgment can accelerate scientific discovery by enabling efficient evaluation, refinement, and comparison of research ideas. While large language models are incre…
Encoded but Not Routed: Explaining the Table-Chart Gap in Scientific Claim Verification
Sunisth Kumar, Xanh Ho, Tim Schopf +3
Multimodal LLMs are increasingly used to assist scientific peer review, where a core requirement is verifying whether claims in a paper are supported by its evidence. Prior work ha…
Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas
Tim Schopf, Michael Färber
Judging the novelty of research ideas is crucial for advancing science, enabling the identification of unexplored directions, and ensuring contributions meaningfully extend existin…
Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models
Tobias Schreieder, Tim Schopf, Michael Färber
The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness. As a result, a growing body of research focuses on…
DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations
Nicholas Popovič, Ashish Kangen, Tim Schopf +1
Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic,…
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes
Tim Schopf, Alexander Blatzheim, Nektarios Machner +1
Scientific document classification is a critical task and often involves many classes. However, collecting human-labeled data for many classes is expensive and usually leads to lab…
An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry
Stephen Meisenbacher, Tim Schopf, Weixin Yan +2
The task of is often an important initial step in unsupervised information extraction, forming the basis for tasks such as topic modeling or document…
NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing
Tim Schopf, Florian Matthes
Scientific literature searches are often exploratory, whereby users are not yet familiar with a particular field or concept but are interested in learning more about it. However, e…
Enterprise Use Cases Combining Knowledge Graphs and Natural Language Processing
Phillip Schneider, Tim Schopf, Juraj Vladika +1
Knowledge management is a critical challenge for enterprises in today's digital world, as the volume and complexity of data being generated and collected continue to grow incessant…
A Knowledge Graph Approach for Exploratory Search in Research Institutions
Tim Schopf, Nektarios Machner, Florian Matthes
Over the past decades, research institutions have grown increasingly and consequently also their research output. This poses a significant challenge for researchers seeking to unde…
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…
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…