Identifying machine-paraphrased plagiarism
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 …
integrity. To enable the detection of machine-paraphrased text, we evaluate the …
[HTML][HTML] A multi-dimensional relation model for dimensional sentiment analysis
Dimensional sentiment analysis has received considerable attention because it can
represent affective states as continuous numerical values on multiple dimensions such as …
represent affective states as continuous numerical values on multiple dimensions such as …
Paraphrase detection: Human vs. machine content
The growing prominence of large language models, such as GPT-4 and ChatGPT, has led to
increased concerns over academic integrity due to the potential for machine-generated …
increased concerns over academic integrity due to the potential for machine-generated …
Predicting determinants influencing user satisfaction with mental health app: An explainable machine learning approach based on unstructured data
In the contemporary digital landscape, the rising concern for mental health has sparked a
surge in the use of mental health apps (MHAs) as accessible tools for addressing …
surge in the use of mental health apps (MHAs) as accessible tools for addressing …
[HTML][HTML] Math-word embedding in math search and semantic extraction
A Greiner-Petter, A Youssef, T Ruas, BR Miller… - Scientometrics, 2020 - Springer
Word embedding, which represents individual words with semantically fixed-length vectors,
has made it possible to successfully apply deep learning to natural language processing …
has made it possible to successfully apply deep learning to natural language processing …
Testing the generalization of neural language models for COVID-19 misinformation detection
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 …
COVID-19 pandemic. Computational support to identify false information within the massive …
Neural topic-enhanced cross-lingual word embeddings for CLIR
Cross-lingual information retrieval (CLIR) methods have quickly made the transition from
translation-based approaches to semantic-based approaches. In this paper, we examine the …
translation-based approaches to semantic-based approaches. In this paper, we examine the …
Incorporating word sense disambiguation in neural language models
We present two supervised (pre-) training methods to incorporate gloss definitions from
lexical resources into neural language models (LMs). The training improves our models' …
lexical resources into neural language models (LMs). The training improves our models' …
A new word embedding model integrated with medical knowledge for deep learning-based sentiment classification
The development of intelligent systems that use social media data for decision-making
processes in numerous domains such as politics, business, marketing, and finance, has …
processes in numerous domains such as politics, business, marketing, and finance, has …
Specialized document embeddings for aspect-based similarity of research papers
Document embeddings and similarity measures underpin content-based recommender
systems, whereby a document is commonly represented as a single generic embedding …
systems, whereby a document is commonly represented as a single generic embedding …