Search Results for author:
Found 22 papers, 12 papers with code
DFKI SLT at GermEval 2021: Multilingual Pre-training and Data Augmentation for the Classification of Toxicity in Social Media Comments
Finally, the combination of the two techniques allows us to obtain an F1 score of 0. 6899 with XLM- RoBERTa and 0. 6859 with MT5.
Semantic Relations between Text Segments for Semantic Storytelling: Annotation Tool - Dataset - Evaluation
Essential is the automated processing of text segments extracted from different content resources by identifying the relevance of a text segment to a topic and its semantic relation to other text segments.
Fine-grained Classification of Political Bias in German News: A Data Set and Initial Experiments
We present a data set consisting of German news articles labeled for political bias on a five-point scale in a semi-supervised way.
Generating Extended and Multilingual Summaries with Pre-trained Transformers
Almost all summarisation methods and datasets focus on a single language and short summaries.
Claim Extraction and Law Matching for COVID-19-related Legislation
We investigate an automated approach to extract legal claims from news articles and to match the claims with their corresponding applicable laws.
Investigating Gender Bias in Turkish Language Models
However, prior research has primarily focused on the English language, especially in the context of gender bias.
Tokenizer Choice For LLM Training: Negligible or Crucial?
The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot.
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge
Generic sentence embeddings provide a coarse-grained approximation of semantic textual similarity but ignore specific aspects that make texts similar.
Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning
To address this problem, we introduce a cross-lingual and progressive transfer learning approach, called CLP-Transfer, that transfers models from a source language, for which pretrained models are publicly available, like English, to a new target language.
Specialized Document Embeddings for Aspect-based Similarity of Research Papers
We compare and analyze three generic document embeddings, six specialized document embeddings and a pairwise classification baseline in the context of research paper recommendations.
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information
Using various experimental settings on three datasets (i. e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model outperforms a strong baseline collectively, which differs from our model only in that the hierarchical structure information is not injected.
Ranked #12 on
Text Summarization
on Pubmed
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics.
Ranked #1 on
Document Classification
on SciDocs (MeSH)
A Qualitative Evaluation of User Preference for Link-based vs. Text-based Recommendations of Wikipedia Articles
We conclude that users of literature recommendation systems can benefit most from hybrid approaches that combine both link- and text-based approaches, where the user's information needs and preferences should control the weighting for the approaches used.
Evaluating Document Representations for Content-based Legal Literature Recommendations
Simultaneously, legal recommender systems are typically evaluated in small-scale user study without any public available benchmark datasets.
Aspect-based Document Similarity for Research Papers
Our findings motivate future research of aspect-based document similarity and the development of a recommender system based on the evaluated techniques.
Contextual Document Similarity for Content-based Literature Recommender Systems
In this doctoral thesis, we explore contextual document similarity measures, i. e., methods that determine document similarity as a triple of two documents and the context of their similarity.
Towards an Open Platform for Legal Information
Recent advances in the area of legal information systems have led to a variety of applications that promise support in processing and accessing legal documents.
Towards Discourse Parsing-inspired Semantic Storytelling
Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection.
QURATOR: Innovative Technologies for Content and Data Curation
In all domains and sectors, the demand for intelligent systems to support the processing and generation of digital content is rapidly increasing.
Named Entities in Medical Case Reports: Corpus and Experiments
We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central's open access library.
Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles
In this paper, we model the problem of finding the relationship between two documents as a pairwise document classification task.
Enriching BERT with Knowledge Graph Embeddings for Document Classification
In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata.
