67 citations · 361 across the 22 of their papers we have counts for
8 papers · 2 filters
Detecting Cross-Language Plagiarism using Open Knowledge Graphs
Johannes Stegmüller, Fabian Bauer-Marquart, Norman Meuschke +3
Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cros…
Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection
Jan Philip Wahle, Nischal Ashok, Terry Ruas +3
A drastic rise in potentially life-threatening misinformation has been a by-product of the COVID-19 pandemic. Computational support to identify false information within the massive…
Incorporating Word Sense Disambiguation in Neural Language Models
Jan Philip Wahle, Terry Ruas, Norman Meuschke +1
We present two supervised (pre-)training methods to incorporate gloss definitions from lexical resources into neural language models (LMs). The training improves our models' perfor…
Evaluating Document Representations for Content-based Legal Literature Recommendations
Malte Ostendorff, Elliott Ash, Terry Ruas +3
Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflec…
Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
Jan Philip Wahle, Terry Ruas, Norman Meuschke +1
The rise of language models such as BERT allows for high-quality text paraphrasing. This is a problem to academic integrity, as it is difficult to differentiate between original an…
Identifying Machine-Paraphrased Plagiarism
Jan Philip Wahle, Terry Ruas, Tomáš Foltýnek +2
Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectivenes…
Enhanced word embeddings using multi-semantic representation through lexical chains
Terry Ruas, Charles Henrique Porto Ferreira, William Grosky +2
The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose t…
Multi-sense embeddings through a word sense disambiguation process
Terry Ruas, William Grosky, Akiko Aizawa
Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they prove…