Computational Linguistics Commons^™

Understanding Perceptions Of A Peruvian Local Market Program: A Reflexive Thematic Analysis, Rosmery Ramos-Sandoval Dr., Jano Ramos-Diaz

The Qualitative Report

Despite growing global interest in short food supply chains (SFSCs), little is known about how consumers in developing countries perceive these models, especially through digital platforms like social media. This study investigates how Twitter users represent and perceive SFSCs in the context of the Peruvian government´s “De la Chacra a la Olla” program. The study analyzed 1,167 tweets from Peruvian Twitter users referencing the hashtag #DeLaChacraALaOlla between 2014 and 2020, using reflexive thematic analysis within an exploratory case study framework to examine consumer perceptions of SFSCs. The analysis revealed three key themes in consumer perceptions of SFSCs on Twitter: direct …

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …

Fact-Checking As A Multi-Step Process: From Ambiguity Resolution To Claim Validation, Wenbo Wang

Dissertations

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. Fact-checking—the process of verifying claims against credible evidence—has emerged as a critical safeguard against misinformation. Yet, the task is fraught with challenges: claims are often ambiguous, context-dependent, or composed of multiple intertwined assertions, while automated systems struggle to replicate the nuanced reasoning of human experts. This dissertation addresses these challenges by reimagining fact-checking as a multi-step, knowledge-guided process that systematically resolves ambiguity, decomposes complexity, and validates claims through structured reasoning. Additionally, the proposed …

On The Provenance Of Software Systems: Automating Software Traceability With Knowledge Graph And Large Language Model Synergy, Tyler Procko

Doctoral Dissertations and Master's Theses

The present dissertation delineates a system that enables those engaged in software development to automatically generate and maintain project life cycle provenance. All projects are implemented and made manifest with the development of artifacts, e.g., papers, code files, etc. Tools exist to accelerate artifact creation, but little focus is paid to the processes that produce them. In terms of Ontology, or, from Ancient Greek, the study of being, the two most basic entities in reality are Continuant and Occurrent, or, roughly, “Artifact” and “Process”. This dissertation posits that for any created artifact, its process of creation, i.e., its life …

Investigating Post-Adoption Abandonment Of Mental Health Mobile Applications Among Young Adults, Donald Harris

Electronic Theses & Dissertations (2024 - present)

The rising prevalence of mental health issues among young adults has driven increased interest in Mental Health Mobile Applications (MHMAs), which offer accessible and cost-effective solutions to traditional barriers such as financial limitations, stigma, and restricted healthcare access. Despite their promise, MHMAs frequently experience high rates of attrition and abandonment, significantly limiting their long-term effectiveness. Employing a mixed-methods, multi-stage research design, this dissertation explores the determinants of MHMA abandonment among young adults, emphasizing the interplay between technological inhibitors and enablers, individual user characteristics, and the mediating roles of user satisfaction and perceived usefulness.

Study 1 utilized quantitative text analysis, including …

Modeling Context And The Characteristica Universalis, John Kausch

Proceedings from the Document Academy

This paper proposes prototypes for the exploration of the context of terms in a knowledge organization system by visualizing machine learning produced word embeddings. It puts this work in the context of the search for a universal language, typified by Leibniz’s characteristica universalis. This tradition of the search for universal languages is put in the context of universalizing tendencies in taxonomic classification in library and information science. Following this there is a discussion of the use of machine learning models to represent context. These two concerns inform the construction of prototypes for exploring the contextual spaces produced by word embeddings …

Executive Order On The Safe, Secure, And Trustworthy Development And Use Of Artificial Intelligence, Joseph R. Biden

Copyright, Fair Use, Scholarly Communication, etc.

Section 1. Purpose. Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use has the potential to help solve urgent challenges while making our world more prosperous, productive, innovative, and secure. At the same time, irresponsible use could exacerbate societal harms such as fraud, discrimination, bias, and disinformation; displace and disempower workers; stifle competition; and pose risks to national security. Harnessing AI for good and realizing its myriad benefits requires mitigating its substantial risks. This endeavor demands a society-wide effort that includes government, the private sector, academia, and civil society.

My Administration places the highest urgency …

Research On Topic Discovery And Evolution Trend Based On Temporal Keyword Characteristics Analysis, Shuqing Li, Juntao Zhu, Wan Wang

Journal of Scientific Information Research

[Purpose/significance]Excavating the research topics in a large number of articles, sorting out the evolution context and correlation of the research topics, predicting the frontier hot spots of the topics can be helpful to enhance the scientificity and vividness of the evolution results.[Method/precess]This paper puts forward the concept of time series influence factor as an important feature in keyword extraction, uses the method of time window to mine and identify topics by using topic model, and makes visual analysis. By applying time series model in the field of deep learning, the purpose of predicting topic popularity is achieved.[Result/concluson]It is verified that …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander

School of Business: Faculty Publications and Other Works

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, …

Misinformation And Disinformation: Detecting Fakes With The Eye And Ai, Victoria Rubin

Data and Test Instruments

How do we detect, deter, and prevent the spread of mis- and disinformationwith the human eye and AI? How does theory inform the practice, and how do theevidence-based research and best practices in lie-catching and truth-seekingprofessions—inform AI? The book looks into well-established human practicessuch as the routines and processes used in detective work, journalism, and scientificinquiry, and how they contribute toward innovative AI solutions. The book explainsthe principles, inner workings, and recent evolution of five types of state-of-the-artAI technologies suitable for curtailing the spread of mis- and disinformation:automated deception detectors, clickbait detectors, satirical fake detectors, rumordebunkers, and computational fact-checking tools.

The Public Innovations Explorer: A Geo-Spatial & Linked-Data Visualization Platform For Publicly Funded Innovation Research In The United States, Seth Schimmel

Dissertations, Theses, and Capstone Projects

The Public Innovations Explorer (https://sethsch.github.io/innovations-explorer/app/index.html) is a web-based tool created using Node.js, D3.js and Leaflet.js that can be used for investigating awards made by Federal agencies and departments participating in the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) grant-making programs between 2008 and 2018. By geocoding the publicly available grants data from SBIR.gov, the Public Innovations Explorer allows users to identify companies performing publicly-funded innovative research in each congressional district and obtain dynamic district-level summaries of funding activity by agency and year. Applying spatial clustering techniques on districts' employment levels across major economic sectors provides users …

Otrouha: A Corpus Of Arabic Etds And A Framework For Automatic Subject Classification, Eman Abdelrahman, Fatimah Alotaibi, Edward A. Fox, Osman Balci

The Journal of Electronic Theses and Dissertations

Although the Arabic language is spoken by more than 300 million people and is one of the six official languages of the United Nations (UN), there has been less research done on Arabic text data (compared to English) in the realm of machine learning, especially in text classification. In the past decade, Arabic data such as news, tweets, etc. have begun to receive some attention. Although automatic text classification plays an important role in improving the browsability and accessibility of data, Electronic Theses and Dissertations (ETDs) have not received their fair share of attention, in spite of the huge number …

Identifying Facets Of Reader-Generated Online Reviews Of Children’S Books Based On A Textual Analysis Approach, Yunseon Choi, Soohyung Joo

Information Science Faculty Publications

With the increasing popularity of social media, online reviews have become one of the primary information sources for book selection. Prior studies have analyzed online reviews, mostly in the domain of business. However, little research has examined the content of online book reviews of children’s books. Book reviews generated by book readers contain different aspects of information, such as opinions, feedback, or emotional responses, from the perspectives of readers. This study explores what aspects of the books are addressed in readers’ reviews, and then it intends to identify categorical features or facets of online book reviews of children’s books. We …

Scholarly Communication And Documentary Fragmentations In The Public Space: A Functional Citation Study, Fidelia Ibekwe, Lucie Loubère

Proceedings from the Document Academy

This paper studies how academic content published in Open Edition.org, an online publication platform in the Social Sciences and Humanities is re-appropriated by members of the public. Our research is therefore concerned with the public appropriation of science and Open science. After extracting the contexts of citation of these content and mapping them, we propose a typology of citation functions as well as of citers (their origins and types). Our preliminary results indicated that academic literature is repurposed and cited by members of the public mainly as scientific warrant (support for their argumentation). We also found that academic content is …

Detecting Clickbait: Here’S How To Do It, Christopher Brogly, Victoria Rubin

Data and Test Instruments

Automatic clickbait detection is a relatively novel task in natural language processing (NLP) and machine learning (ML). “Clickbait” is a hyperlink created primarily to attract attention to its target content. This article introduces a binary classifier, the Language and Information Technology Research Lab (LiT.RL, pronounced “literal”) Clickbait Detector, which automatically distinguishes clickbait from nonclickbait. We used NLP and ML for 38 textual features, contrasting clickbait with “headlinese.” When tested on 11,000 hyperlinks, it achieves 94 per cent accuracy using a support vector machine. Integrated with the LiT.RL News Verification Browser, a downloadable stand-alone research tool, the Clickbait Detector user interface …

Towards News Verification: Deception Detection Methods For News Discourse, Yimin Chen, Victoria L. Rubin, Niall Conroy

FIMS Presentations

News verification is a process of determining whether a particular news report is truthful or deceptive. Deliberately deceptive (fabricated) news creates false conclusions in the readers’ minds. Truthful (authentic) news matches the writer’s knowledge. How do you tell the difference between the two in an automated way? To investigate this question, we analyzed rhetorical structures, discourse constituent parts and their coherence relations in deceptive and truthful news sample from NPR’s “Bluff the Listener”. Subsequently, we applied a vector space model to cluster the news by discourse feature similarity, achieving 63% accuracy. Our predictive model is not significantly better than chance …

Metadata And Linked Data In Word Sense Disambiguation, Matthew Corsmeier

Library Philosophy and Practice (e-journal)

Word Sense Disambiguation (WSD) can be assisted by taking advantage of the metadata embedded in the various ontologies, lexica, databases, etc… that exist in the Semantic Web. Automated processes that exploit the links already present in the Semantic Web can strengthen parsing of word senses by using user-contributed and semantically-linked data. These processes are only possible because of a commitment to interoperability and the creation of shared standards. This paper will review some of the most heavily used Linguistic Linked Open Data (LLOD) tools and models which show the most promise for using metadata to alleviate problems caused by polysemous …

Study Of Stemming Algorithms, Savitha Kodimala

UNLV Theses, Dissertations, Professional Papers, and Capstones

Automated stemming is the process of reducing words to their roots. The stemmed words are typically used to overcome the mismatch problems associated with text searching.

In this thesis, we report on the various methods developed for stemming. In particular, we show the effectiveness of n-gram stemming methods on a collection of documents.

Computational Linguistics For Metadata Building: Aggregating Text Processing Technologies For Enhanced Image Access, Judith Klavans, Carolyn Sheffield, Eileen Abels, Joan E. Beaudoin, Laura Jenemann, Jimmy Lin, Tom Lippincott, Rebecca Passonneau, Tandeep Sidhu, Dagobert Soergel, Tae Yano

School of Information Sciences Faculty Research Publications

We present a system which applies text mining using computational linguistic techniques to automatically extract, categorize, disambiguate and filter metadata for image access. Candidate subject terms are identified through standard approaches; novel semantic categorization using machine learning and disambiguation using both WordNet and a domain specific thesaurus are applied. The resulting metadata can be manually edited by image catalogers or filtered by semi-automatic rules. We describe the implementation of this workbench created for, and evaluated by, image catalogers. We discuss the system's current functionality, developed under the Computational Linguistics for Metadata Building (CLiMB) research project. The CLiMB Toolkit has been …