Search Results for author:
Found 25 papers, 3 papers with code
A Study of Implicit Bias in Pretrained Language Models against People with Disabilities
Pretrained language models (PLMs) have been shown to exhibit sociodemographic biases, such as against gender and race, raising concerns of downstream biases in language technologies.
This Table is Different: A WordNet-Based Approach to Identifying References to Document Entities
Writing intended to inform frequently contains references to document entities (DEs), a mixed class that includes orthographically structured items (e. g., illustrations, sections, lists) and discourse entities (arguments, suggestions, points).
A Tale of Two Regulatory Regimes: Creation and Analysis of a Bilingual Privacy Policy Corpus
Over the past decade, researchers have started to explore the use of NLP to develop tools aimed at helping the public, vendors, and regulators analyze disclosures made in privacy policies.
STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents
Therefore, we propose the first title-text dataset on web documents that incorporates a wide variety of domains to facilitate downstream training.
"Confidently Nonsensical?'': A Critical Survey on the Perspectives and Challenges of 'Hallucinations' in NLP
We investigate how hallucination in large language models (LLM) is characterized in peer-reviewed literature using a critical examination of 103 publications across NLP research.
Automated Detection and Analysis of Data Practices Using A Real-World Corpus
Privacy policies are crucial for informing users about data practices, yet their length and complexity often deter users from reading them.
The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis
We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets.
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias
We apply CALM to 20 large language models, and find that for 2 language model series, larger parameter models tend to be more biased than smaller ones.
Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles
We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods.
Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models
We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD).
Sociodemographic Bias in Language Models: A Survey and Forward Path
This paper presents a comprehensive survey of work on sociodemographic bias in language models (LMs).
Nationality Bias in Text Generation
Little attention is placed on analyzing nationality bias in language models, especially when nationality is highly used as a factor in increasing the performance of social NLP models.
Creation and Analysis of an International Corpus of Privacy Laws
To examine the status and evolution of this patchwork, we introduce the Government Privacy Instructions Corpus, or GPI Corpus, of 1, 043 privacy laws, regulations, and guidelines, covering 182 jurisdictions.
Automated Detection of Doxing on Twitter
Doxing refers to the practice of disclosing sensitive personal information about a person without their consent.
Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models
The results show that all exhibit strong negative biases on sentences that mention disability.
Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?
Privacy plays a crucial role in preserving democratic ideals and personal autonomy.
A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and Application Source
We widely use emojis in social networking to heighten, mitigate or negate the sentiment of the text.
Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies
Organisations disclose their privacy practices by posting privacy policies on their website.
Question Answering for Privacy Policies: Combining Computational and Legal Perspectives
The PrivacyQA corpus offers a challenging corpus for question answering, with genuine real-world utility.
Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents
To remedy this, we present a flexible system for automatically extracting the hierarchical section titles and prose organization of web documents irrespective of differences in HTML representation.
Identifying the Provision of Choices in Privacy Policy Text
Our techniques enable the creation of systems to help Internet users to learn about their choices, thereby effectuating notice and choice and improving Internet privacy.
