We introduce CoDe-KG, an open-source, end-to-end pipeline for extracting sentence-level knowledge graphs by combining robust coreference resolution with syntactic sentence decomposition. Using our model, we contribute a dataset of over 150,000 knowledge triples, which is open source...

Illegal wildlife trade is being increasingly conducted through online channels, posing a significant risk to global biodiversity and environmental sustainability. In this paper, we propose a method that employs Vision-Language Models (VLMs) and Large Language Models (LLMs) to analyze online advertisements for wildlife products and generate a description that includes the product type, species, and its status on the IUCN red list and CITES appendices...

Wildlife trafficking remains a critical global issue, significantly impacting biodiversity, ecological stability, and public health. Despite efforts to combat this illicit trade, the rise of e-commerce platforms has made it easier to sell wildlife products, putting new pressure on wild populations of endangered and threatened species...

This research focuses on advancing RE methodologies by employing and comparing various NLP models for analyzing medical relationships, particularly concerning Gastroesophageal Reflux Disease (GERD). Leveraging a comprehensive dataset of GERD-related articles from PubMed, the study explores the effectiveness of SpaCy for Named Entity Recognition (NER) and BERT-based models (including Bio-BERT and ELECTRA) for tokenization and deep learning classification tasks...

Since COVID-19, the illegal wildlife trade (IWT) has made a massive transition from physical to online marketplaces, creating new challenges for identifying and tracking the trade of reptile leather products. Social network analysis has been used in the past to identify networks of key actors and generate strategies to dismantle these networks...

Illegal wildlife trade is being increasingly conducted through online channels, posing a significant risk to global biodiversity and environmental sustainability. In this paper, we propose a method that employs Vision-Language Models (VLMs) and Large Language Models (LLMs) to analyze online advertisements for wildlife products and generate a description that includes the product type, species, and its status on the IUCN red list and CITES appendices...

Direct exploitation, which includes the trade of wild animals for their parts, is a major driver of extinction. Digital communication tools, particularly the internet, have facilitated the trade in endangered species...

Wildlife trafficking remains a critical global issue, significantly impacting biodiversity, ecological stability, and public health. Despite efforts to combat this illicit trade, the rise of e-commerce platforms has made it easier to sell wildlife products, putting new pressure on wild populations of endangered and threatened species...

Wildlife traffickers are increasingly carrying out their activities in cyberspace. As they advertise and sell wildlife products in online marketplaces, they leave digital traces of their activity...

Human-wildlife conflict (HWC) is one of the most pressing conservation issues at present, with incidents leading to human injury and death, crop and property damage, and livestock predation. Since acquiring real-time data and performing manual analysis on those incidents are costly, we propose to leverage machine learning techniques to build an automated pipeline to construct an HWC knowledge base from historical news articles...

We conducted research to begin the development of CATpc: Critical Activity Teacher Planning Companion. CATpc is a generative AI (genAI) chatbot companion for teachers to support them in adapting and creating culturally focused and universally designed STEM activities and learning environments...

African American Vernacular English (AAVE) is a dialect of the English language spoken in the United States by members of the Black community. The stark differences between AAVE and Standard American English (SAE), as well as a historically negative stigma towards its use, have contributed to an academic performance gap between Black students and their non-Black counterparts...

Large Language Models (LLMs) generate fluent answers but can struggle with trustworthy, domain-specific reasoning. We evaluate whether domain knowledge graphs (KGs) improve Retrieval-Augmented Generation (RAG) for healthcare by constructing three PubMed-derived graphs...

Understanding causality is a longstanding goal across many different domains. Different articles, such as those published in medical journals, disseminate newly discovered knowledge that is often causal...

Understanding the genome of Cannabis sativa holds significant scientific value due to the multi-faceted therapeutic nature of the plant. Links from cannabis gene to therapeutic property are important to establish gene targets for the optimization of specific therapeutic properties through selective breeding of cannabis strains...

Prior studies show convolutional neural networks predicting self-reported race using x-rays of chest, hand and spine, chest computed tomography, and mammogram. We seek an understanding of the mechanism that reveals race within x-ray images, investigating the possibility that race is not predicted using the physical structure in x-ray images but is embedded in the grayscale pixel intensities...

Understanding causality is a longstanding goal across many different domains. Different articles, such as those published in medical journals, publish newly discovered knowledge, often causal...

Research articles published in medical journals often present findings from causal experiments. In this paper, we use this intuition to build a model that leverages causal relations expressed in text to unearth factors related to Sjögren's syndrome...

Discovering causal knowledge is an important aspect of much scientific research and such findings are often recorded in scholarly articles. Automatically identifying such knowledge from article text can be a useful tool and can act as an impetus for further research on those topics...

Breast cancer patients go through many ordeals when they undergo treatments. Many of these issues are personal, social, or professional. As many of them are not directly medical in nature, these issues are not discussed with their healthcare providers...

Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions...

Human-wildlife conflict (HWC) is one of the most pressing conservation issues at present, with incidents leading to human injury and death, crop and property damage, and livestock predation. Since acquiring real-time data and performing manual analysis on those incidents are costly, we propose to leverage machine learning techniques to build an automated pipeline to construct an HWC knowledge base from historical news articles...

Citizens' perception of politicians and political issues is increasingly influenced by social media. However, little is known about the potential of second order effects of social media in parts of the world where the majority of voting citizens are not online...

Emergence of digital news provides new opportunities in information extraction. Proper characterization of unstructured news can help identify signals that may drive variations in many observable phenomena, such as disease outbreaks...

This study measures the effects of Federal Open Market Committee text content on the direction of short- and medium-term interest rate movements. Because the words relevant to short- and medium-term interest rates differ, we apply a supervised approach to learn distinct sets of topics for each dependent variable being examined...

Many socio-economic indicators are sensitive to real-world events. Proper characterization of the events can help to identify the relevant events that drive fluctuations in these indicators...

In this mixed-methods study of political discourse, we study the affordances of Twitter in the context of free speech in India. We critically examine specific cases of the legal prosecution of free speech and the use of extreme speech in attacks on people to document the risks to citizens when they engage in antagonistic online discourse, particularly against the state or political institutions...

Collecting data from representative users with disabilities for accessibility research is time and resource consuming. With the proliferation of social media websites, many online spaces have emerged for people with disabilities...

In this work-in-progress paper, we examine how the campaign to end the guardianship law in Saudi Arabia is being discussed and debated on social media. Through a content analysis of tweets, we first identify those with either a positive or negative sentiment towards ending the law and then we identify topical themes across these sentiment categories...

Citizens' perception of politicians and political issues is increasingly influenced by social media. However, little is known about the potential of second order effects of social media in parts of the world where the majority of voting citizens are not online...