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Found 30 papers, 24 papers with code
Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts
Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0. 804, outperforming existing methods.
Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains
For our approach, we develop two kinds of lexical chains: (i) a multilevel flexible chain representation of the extracted semantic values, which is used to construct a fixed segmentation of these chains and constituent words in the text; and (ii) a fixed lexical chain obtained directly from the initial semantic representation from a document.
What's under the hood: Investigating Automatic Metrics on Meeting Summarization
Meeting summarization has become a critical task considering the increase in online interactions.
MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions
MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models.
Citation Amnesia: NLP and Other Academic Fields Are in a Citation Age Recession
This study examines the tendency to cite older work across 20 fields of study over 43 years (1980--2023).
The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias
However, we have identified a lack of interdisciplinarity in existing projects, and a need for more awareness of the various types of media bias to support methodologically thorough performance evaluations of media bias detection systems.
Paraphrase Types for Generation and Detection
Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language.
We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields
We analyzed ~77k NLP papers, ~3. 1m citations from NLP papers to other papers, and ~1. 8m citations from other papers to NLP papers.
The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research
Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development.
Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection
A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.
Paraphrase Detection: Human vs. Machine Content
Additionally, we identify four datasets as the most diverse and challenging for paraphrase detection.
Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles
Media has a substantial impact on the public perception of events.
Analyzing Multi-Task Learning for Abstractive Text Summarization
Further, we find that choice and combinations of task families influence downstream performance more than the training scheme, supporting the use of task families for abstractive text summarization.
CS-Insights: A System for Analyzing Computer Science Research
This paper presents CS-Insights, an interactive web application to analyze computer science publications from DBLP through multiple perspectives.
How Large Language Models are Transforming Machine-Paraphrased Plagiarism
The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work.
Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts
Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0. 804, outperforming existing methods.
A Domain-adaptive Pre-training Approach for Language Bias Detection in News
We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain which identifies sentence-level bias with an F1 score of 0. 814.
D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research
We present an initial analysis focused on the volume of computer science research (e. g., number of papers, authors, research activity), trends in topics of interest, and citation patterns.
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.
Detecting Cross-Language Plagiarism using Open Knowledge Graphs
Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations.
Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection
We expect that evaluating a broad spectrum of datasets and models will benefit future research in developing misinformation detection systems.
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).
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.
Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
The rise of language models such as BERT allows for high-quality text paraphrasing.
Identifying Machine-Paraphrased Plagiarism
Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity.
Enhanced word embeddings using multi-semantic representation through lexical chains
The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually.
Multi-sense embeddings through a word sense disambiguation process
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 proved themselves able to capture and represent semantic relationships from massive amounts of data.
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.
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.
Why Machines Cannot Learn Mathematics, Yet
Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods.
