Title stringlengths 14 179 | Authors stringlengths 6 464 | Abstract stringlengths 83 1.93k | entry_id stringlengths 32 34 | Date timestamp[ns, tz=UTC] | Categories stringlengths 5 168 | year int32 2.01k 2.02k |
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DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM | Weijie Xu, Wenxiang Hu, Fanyou Wu, Srinivasan Sengamedu | In the burgeoning field of natural language processing (NLP), Neural Topic Models (NTMs) , Large Language Models (LLMs) and Diffusion model have emerged as areas of significant research interest. Despite this, NTMs primarily utilize contextual embeddings from LLMs, which are not optimal for clustering or capable for to... | http://arxiv.org/abs/2310.15296v2 | 2023-10-23T19:03:04Z | cs.CL, cs.AI, 68T50, I.2.7 | 2,023 |
Unraveling the Skillsets of Data Scientists: Text Mining Analysis of Dutch University Master Programs in Data Science and Artificial Intelligence | Mathijs J. Mol, Barbara Belfi, Zsuzsa Bakk | The growing demand for data scientists in the global labor market and the Netherlands has led to a rise in data science and artificial intelligence (AI) master programs offered by universities. However, there is still a lack of clarity regarding the specific skillsets of data scientists. This study aims to address this... | http://arxiv.org/abs/2310.14726v1 | 2023-10-23T09:02:44Z | stat.OT | 2,023 |
Tracking electricity losses and their perceived causes using nighttime light and social media | Samuel W Kerber, Nicholas A Duncan, Guillaume F LHer, Morgan Bazilian, Chris Elvidge, Mark R Deinert | Urban environments are intricate systems where the breakdown of critical infrastructure can impact both the economic and social well-being of communities. Electricity systems hold particular significance, as they are essential for other infrastructure, and disruptions can trigger widespread consequences. Typically, ass... | http://arxiv.org/abs/2310.12346v1 | 2023-10-18T21:44:39Z | physics.soc-ph, cs.LG, cs.SI | 2,023 |
Cross-Platform Social Dynamics: An Analysis of ChatGPT and COVID-19 Vaccine Conversations | Shayan Alipour, Alessandro Galeazzi, Emanuele Sangiorgio, Michele Avalle, Ljubisa Bojic, Matteo Cinelli, Walter Quattrociocchi | The role of social media in information dissemination and agenda-setting has significantly expanded in recent years. By offering real-time interactions, online platforms have become invaluable tools for studying societal responses to significant events as they unfold. However, online reactions to external developments ... | http://arxiv.org/abs/2310.11116v1 | 2023-10-17T09:58:55Z | cs.CY, physics.soc-ph | 2,023 |
A Large-Scale Exploratory Study of Android Sports Apps in the Google Play Store | Bhagya Chembakottu, Heng Li, Foutse Khomh | Prior studies on mobile app analysis often analyze apps across different categories or focus on a small set of apps within a category. These studies either provide general insights for an entire app store which consists of millions of apps, or provide specific insights for a small set of apps. However, a single app cat... | http://arxiv.org/abs/2310.07921v1 | 2023-10-11T22:28:53Z | cs.SE | 2,023 |
Refined Mechanism Design for Approximately Structured Priors via Active Regression | Christos Boutsikas, Petros Drineas, Marios Mertzanidis, Alexandros Psomas, Paritosh Verma | We consider the problem of a revenue-maximizing seller with a large number of items $m$ for sale to $n$ strategic bidders, whose valuations are drawn independently from high-dimensional, unknown prior distributions. It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously... | http://arxiv.org/abs/2310.07874v1 | 2023-10-11T20:34:17Z | cs.GT, cs.DS, cs.IR, cs.LG | 2,023 |
Document-Level Supervision for Multi-Aspect Sentiment Analysis Without Fine-grained Labels | Kasturi Bhattacharjee, Rashmi Gangadharaiah | Aspect-based sentiment analysis (ABSA) is a widely studied topic, most often trained through supervision from human annotations of opinionated texts. These fine-grained annotations include identifying aspects towards which a user expresses their sentiment, and their associated polarities (aspect-based sentiments). Such... | http://arxiv.org/abs/2310.06940v1 | 2023-10-10T18:53:21Z | cs.CL | 2,023 |
Sparse topic modeling via spectral decomposition and thresholding | Huy Tran, Yating Liu, Claire Donnat | The probabilistic Latent Semantic Indexing model assumes that the expectation of the corpus matrix is low-rank and can be written as the product of a topic-word matrix and a word-document matrix. In this paper, we study the estimation of the topic-word matrix under the additional assumption that the ordered entries of ... | http://arxiv.org/abs/2310.06730v1 | 2023-10-10T15:54:20Z | stat.ME, 62H12 | 2,023 |
Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation | Xunxin Cai, Meng Xiao, Zhiyuan Ning, Yuanchun Zhou | In addressing the imbalanced issue of data within the realm of Natural Language Processing, text data augmentation methods have emerged as pivotal solutions. This data imbalance is prevalent in the research proposals submitted during the funding application process. Such imbalances, resulting from the varying popularit... | http://arxiv.org/abs/2310.05318v2 | 2023-10-09T00:45:20Z | cs.CL | 2,023 |
TopicAdapt- An Inter-Corpora Topics Adaptation Approach | Pritom Saha Akash, Trisha Das, Kevin Chen-Chuan Chang | Topic models are popular statistical tools for detecting latent semantic topics in a text corpus. They have been utilized in various applications across different fields. However, traditional topic models have some limitations, including insensitivity to user guidance, sensitivity to the amount and quality of data, and... | http://arxiv.org/abs/2310.04978v1 | 2023-10-08T02:56:44Z | cs.CL, cs.LG | 2,023 |
A Process for Topic Modelling Via Word Embeddings | Diego Saldaña Ulloa | This work combines algorithms based on word embeddings, dimensionality reduction, and clustering. The objective is to obtain topics from a set of unclassified texts. The algorithm to obtain the word embeddings is the BERT model, a neural network architecture widely used in NLP tasks. Due to the high dimensionality, a d... | http://arxiv.org/abs/2312.03705v1 | 2023-10-06T15:10:35Z | cs.CL | 2,023 |
Multi-Industry Simplex : A Probabilistic Extension of GICS | Maksim Papenkov, Chris Meredith, Claire Noel, Jai Padalkar, Temple Hendrickson, Daniel Nitiutomo, Thomas Farrell | Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry m... | http://arxiv.org/abs/2310.04280v2 | 2023-10-06T14:27:13Z | q-fin.PM | 2,023 |
HuBERTopic: Enhancing Semantic Representation of HuBERT through Self-supervision Utilizing Topic Model | Takashi Maekaku, Jiatong Shi, Xuankai Chang, Yuya Fujita, Shinji Watanabe | Recently, the usefulness of self-supervised representation learning (SSRL) methods has been confirmed in various downstream tasks. Many of these models, as exemplified by HuBERT and WavLM, use pseudo-labels generated from spectral features or the model's own representation features. From previous studies, it is known t... | http://arxiv.org/abs/2310.03975v1 | 2023-10-06T02:19:09Z | cs.SD, cs.CL | 2,023 |
COVID-19 South African Vaccine Hesitancy Models Show Boost in Performance Upon Fine-Tuning on M-pox Tweets | Nicholas Perikli, Srimoy Bhattacharya, Blessing Ogbuokiri, Zahra Movahedi Nia, Benjamin Lieberman, Nidhi Tripathi, Salah-Eddine Dahbi, Finn Stevenson, Nicola Bragazzi, Jude Kong, Bruce Mellado | Very large numbers of M-pox cases have, since the start of May 2022, been reported in non-endemic countries leading many to fear that the M-pox Outbreak would rapidly transition into another pandemic, while the COVID-19 pandemic ravages on. Given the similarities of M-pox with COVID-19, we chose to test the performance... | http://arxiv.org/abs/2310.04453v1 | 2023-10-04T08:30:22Z | cs.CL, cs.LG, cs.SI | 2,023 |
Finding Pragmatic Differences Between Disciplines | Lee Kezar, Jay Pujara | Scholarly documents have a great degree of variation, both in terms of content (semantics) and structure (pragmatics). Prior work in scholarly document understanding emphasizes semantics through document summarization and corpus topic modeling but tends to omit pragmatics such as document organization and flow. Using a... | http://arxiv.org/abs/2310.00204v1 | 2023-09-30T00:46:14Z | cs.CL | 2,023 |
"ChatGPT, a Friend or Foe for Education?" Analyzing the User's Perspectives on the Latest AI Chatbot Via Reddit | Forhan Bin Emdad, Benhur Ravuri, Lateef Ayinde, Mohammad Ishtiaque Rahman | Latest developments in Artificial Intelligence (AI) and big data gave rise to Artificial Intelligent agents like Open AI's ChatGPT, which has recently become the fastest growing application since Facebook and WhatsApp. ChatGPT has demonstrated its ability to impact students' classroom learning experience and exam outco... | http://arxiv.org/abs/2311.06264v1 | 2023-09-27T23:59:44Z | cs.CY, cs.AI | 2,023 |
Interactive Distillation of Large Single-Topic Corpora of Scientific Papers | Nicholas Solovyev, Ryan Barron, Manish Bhattarai, Maksim E. Eren, Kim O. Rasmussen, Boian S. Alexandrov | Highly specific datasets of scientific literature are important for both research and education. However, it is difficult to build such datasets at scale. A common approach is to build these datasets reductively by applying topic modeling on an established corpus and selecting specific topics. A more robust but time-co... | http://arxiv.org/abs/2309.10772v1 | 2023-09-19T17:18:36Z | cs.IR, cs.CL, cs.DL, cs.LG | 2,023 |
Multi-turn Dialogue Comprehension from a Topic-aware Perspective | Xinbei Ma, Yi Xu, Hai Zhao, Zhuosheng Zhang | Dialogue related Machine Reading Comprehension requires language models to effectively decouple and model multi-turn dialogue passages. As a dialogue development goes after the intentions of participants, its topic may not keep constant through the whole passage. Hence, it is non-trivial to detect and leverage the topi... | http://arxiv.org/abs/2309.09666v1 | 2023-09-18T11:03:55Z | cs.CL | 2,023 |
A Novel Method of Fuzzy Topic Modeling based on Transformer Processing | Ching-Hsun Tseng, Shin-Jye Lee, Po-Wei Cheng, Chien Lee, Chih-Chieh Hung | Topic modeling is admittedly a convenient way to monitor markets trend. Conventionally, Latent Dirichlet Allocation, LDA, is considered a must-do model to gain this type of information. By given the merit of deducing keyword with token conditional probability in LDA, we can know the most possible or essential topic. Ho... | http://arxiv.org/abs/2309.09658v1 | 2023-09-18T10:52:54Z | cs.CL | 2,023 |
Measuring COVID-19 Related Media Consumption on Twitter | Cai Yang | The COVID-19 pandemic has been affecting the world dramatically ever since 2020. The minimum availability of physical interactions during the lockdown has caused more and more people to turn to online activities on social media platforms. These platforms have provided essential updates regarding the pandemic, serving a... | http://arxiv.org/abs/2309.08866v1 | 2023-09-16T04:01:45Z | cs.SI, cs.CY | 2,023 |
Towards the TopMost: A Topic Modeling System Toolkit | Xiaobao Wu, Fengjun Pan, Anh Tuan Luu | Topic models have been proposed for decades with various applications and recently refreshed by the neural variational inference. However, these topic models adopt totally distinct dataset, implementation, and evaluation settings, which hinders their quick utilization and fair comparisons. This greatly hinders the rese... | http://arxiv.org/abs/2309.06908v1 | 2023-09-13T12:10:54Z | cs.CL, cs.AI, cs.IR, cs.LG | 2,023 |
Evaluating Dynamic Topic Models | Charu James, Mayank Nagda, Nooshin Haji Ghassemi, Marius Kloft, Sophie Fellenz | There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure for DTMs that analyzes the changes in the quality of each topic over time. Additionally, we propose an extension combining topic quality wit... | http://arxiv.org/abs/2309.08627v1 | 2023-09-12T13:30:25Z | cs.CL, cs.IR, cs.LG | 2,023 |
A comparison of citation-based clustering and topic modeling for science mapping | Qianqian Xie, Ludo Waltman | Science mapping is an important tool to gain insight into scientific fields, to identify emerging research trends, and to support science policy. Understanding the different ways in which different science mapping approaches capture the structure of scientific fields is critical. This paper presents a comparative analy... | http://arxiv.org/abs/2309.06160v1 | 2023-09-12T12:07:15Z | cs.DL | 2,023 |
A Contextual Topic Modeling and Content Analysis of Iranian laws and Regulations | Zahra Hemmat, Mohammad Mehraeen, Rahmatolloah Fattahi | A constitution is the highest legal document of a country and serves as a guide for the establishment of other laws. The constitution defines the political principles, structure, hierarchy, position, and limits of the political power of a country's government. It determines and guarantees the rights of citizens. This s... | http://arxiv.org/abs/2309.13051v1 | 2023-09-06T18:00:51Z | cs.CY, cs.AI | 2,023 |
What are Public Concerns about ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You | Rui Wang, Xing Liu, Yanan Wang, Haiping Huang | The recently released artificial intelligence conversational agent, ChatGPT, has gained significant attention in academia and real life. A multitude of early ChatGPT users eagerly explore its capabilities and share their opinions on it via social media. Both user queries and social media posts express public concerns r... | http://arxiv.org/abs/2309.01522v2 | 2023-09-04T11:05:10Z | cs.CL | 2,023 |
MPTopic: Improving topic modeling via Masked Permuted pre-training | Xinche Zhang, Evangelos milios | Topic modeling is pivotal in discerning hidden semantic structures within texts, thereby generating meaningful descriptive keywords. While innovative techniques like BERTopic and Top2Vec have recently emerged in the forefront, they manifest certain limitations. Our analysis indicates that these methods might not priori... | http://arxiv.org/abs/2309.01015v1 | 2023-09-02T20:38:58Z | cs.IR, cs.LG | 2,023 |
Insights Into the Nutritional Prevention of Macular Degeneration based on a Comparative Topic Modeling Approach | Lucas Cassiel Jacaruso | Topic modeling and text mining are subsets of Natural Language Processing (NLP) with relevance for conducting meta-analysis (MA) and systematic review (SR). For evidence synthesis, the above NLP methods are conventionally used for topic-specific literature searches or extracting values from reports to automate essentia... | http://arxiv.org/abs/2309.00312v4 | 2023-09-01T07:53:28Z | cs.CL | 2,023 |
BioCoder: A Benchmark for Bioinformatics Code Generation with Contextual Pragmatic Knowledge | Xiangru Tang, Bill Qian, Rick Gao, Jiakang Chen, Xinyun Chen, Mark Gerstein | Pre-trained large language models have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate tasks and to be appropriately specialized to particular domains. Here, we target bioinformatics due to the amount of specialized domain knowledge, ... | http://arxiv.org/abs/2308.16458v4 | 2023-08-31T04:52:58Z | cs.LG, cs.AI, cs.CL | 2,023 |
Classification-Aware Neural Topic Model Combined With Interpretable Analysis -- For Conflict Classification | Tianyu Liang, Yida Mu, Soonho Kim, Darline Larissa Kengne Kuate, Julie Lang, Rob Vos, Xingyi Song | A large number of conflict events are affecting the world all the time. In order to analyse such conflict events effectively, this paper presents a Classification-Aware Neural Topic Model (CANTM-IA) for Conflict Information Classification and Topic Discovery. The model provides a reliable interpretation of classificati... | http://arxiv.org/abs/2308.15232v1 | 2023-08-29T11:40:24Z | cs.LG, cs.CL, cs.IR | 2,023 |
Retractions in Arts and Humanities: an Analysis of the Retraction Notices | Ivan Heibi, Silvio Peroni | The aim of this work is to understand the retraction phenomenon in the arts and humanities domain through an analysis of the retraction notices: formal documents stating and describing the retraction of a particular publication. The retractions and the corresponding notices are identified using the data provided by Ret... | http://arxiv.org/abs/2308.13573v1 | 2023-08-25T08:20:48Z | cs.DL | 2,023 |
Discovering Mental Health Research Topics with Topic Modeling | Xin Gao, Cem Sazara | Mental health significantly influences various aspects of our daily lives, and its importance has been increasingly recognized by the research community and the general public, particularly in the wake of the COVID-19 pandemic. This heightened interest is evident in the growing number of publications dedicated to menta... | http://arxiv.org/abs/2308.13569v1 | 2023-08-25T05:25:05Z | cs.CL, cs.LG | 2,023 |
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model | Yuezhou Zhang, Amos A Folarin, Judith Dineley, Pauline Conde, Valeria de Angel, Shaoxiong Sun, Yatharth Ranjan, Zulqarnain Rashid, Callum Stewart, Petroula Laiou, Heet Sankesara, Linglong Qian, Faith Matcham, Katie M White, Carolin Oetzmann, Femke Lamers, Sara Siddi, Sara Simblett, Björn W. Schuller, Srinivasan Vairava... | Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processing has been employed on social media to predict depression, but limitations remain-lack of validated labels, biased user samples, and no con... | http://arxiv.org/abs/2308.11773v2 | 2023-08-22T20:30:59Z | cs.CL, cs.CY, cs.SD, eess.AS, q-bio.QM | 2,023 |
Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis | Anusuya Krishnan | The exponential growth of online social network platforms and applications has led to a staggering volume of user-generated textual content, including comments and reviews. Consequently, users often face difficulties in extracting valuable insights or relevant information from such content. To address this challenge, m... | http://arxiv.org/abs/2308.11520v1 | 2023-08-19T08:18:04Z | cs.CL, cs.AI | 2,023 |
Wisdom of the Crowds or Ignorance of the Masses? A data-driven guide to WSB | Valentina Semenova, Dragos Gorduza, William Wildi, Xiaowen Dong, Stefan Zohren | A trite yet fundamental question in economics is: What causes large asset price fluctuations? A tenfold rise in the price of GameStop equity, between the 22nd and 28th of January 2021, demonstrated that herding behaviour among retail investors is an important contributing factor. This paper presents a data-driven guide... | http://arxiv.org/abs/2308.09485v1 | 2023-08-18T11:39:21Z | econ.GN, q-fin.EC | 2,023 |
Enhancing API Documentation through BERTopic Modeling and Summarization | AmirHossein Naghshzan, Sylvie Ratte | As the amount of textual data in various fields, including software development, continues to grow, there is a pressing demand for efficient and effective extraction and presentation of meaningful insights. This paper presents a unique approach to address this need, focusing on the complexities of interpreting Applicat... | http://arxiv.org/abs/2308.09070v1 | 2023-08-17T15:57:12Z | cs.SE, cs.AI, cs.CL, cs.LG | 2,023 |
Investigating disaster response through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season | Zihui Ma, Lingyao Li, Libby Hemphill, Gregory B. Baecher, Yubai Yuan | Effective disaster response is critical for affected communities. Responders and decision-makers would benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and demands during a... | http://arxiv.org/abs/2308.05281v2 | 2023-08-10T01:51:33Z | cs.SI, cs.CL, cs.IR, cs.LG | 2,023 |
Social Media, Topic Modeling and Sentiment Analysis in Municipal Decision Support | Miloš Švaňa | Many cities around the world are aspiring to become. However, smart initiatives often give little weight to the opinions of average citizens. Social media are one of the most important sources of citizen opinions. This paper presents a prototype of a framework for processing social media posts with municipal decision... | http://arxiv.org/abs/2308.04124v1 | 2023-08-08T08:27:57Z | cs.CL, cs.SI | 2,023 |
AI Chatbots as Multi-Role Pedagogical Agents: Transforming Engagement in CS Education | Cassie Chen Cao, Zijian Ding, Jionghao Lin, Frank Hopfgartner | This study investigates the use of Artificial Intelligence (AI)-powered, multi-role chatbots as a means to enhance learning experiences and foster engagement in computer science education. Leveraging a design-based research approach, we develop, implement, and evaluate a novel learning environment enriched with four di... | http://arxiv.org/abs/2308.03992v1 | 2023-08-08T02:13:44Z | cs.AI | 2,023 |
Science and engineering for what? A large-scale analysis of students' projects in science fairs | Adelmo Eloy, Thomas Palmeira Ferraz, Fellip Silva Alves, Roseli de Deus Lopes | Science and Engineering fairs offer K-12 students opportunities to engage with authentic STEM practices. Particularly, students are given the chance to experience authentic and open inquiry processes, by defining which themes, questions and approaches will guide their scientific endeavors. In this study, we analyzed da... | http://arxiv.org/abs/2308.02962v2 | 2023-08-05T22:19:03Z | cs.AI, cs.CL, physics.ed-ph, stat.AP | 2,023 |
From Fake to Hyperpartisan News Detection Using Domain Adaptation | Răzvan-Alexandru Smădu, Sebastian-Vasile Echim, Dumitru-Clementin Cercel, Iuliana Marin, Florin Pop | Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two te... | http://arxiv.org/abs/2308.02185v1 | 2023-08-04T07:58:48Z | cs.CL | 2,023 |
What Is the Difference Between a Mountain and a Molehill? Quantifying Semantic Labeling of Visual Features in Line Charts | Dennis Bromley, Vidya Setlur | Relevant language describing visual features in charts can be useful for authoring captions and summaries about the charts to help with readers' takeaways. To better understand the interplay between concepts that describe visual features and the semantic relationships among those concepts (e.g., 'sharp increase' vs. 'g... | http://arxiv.org/abs/2308.01370v1 | 2023-08-02T18:24:11Z | cs.HC | 2,023 |
Deep Dive into the Language of International Relations: NLP-based Analysis of UNESCO's Summary Records | Joanna Wojciechowska, Mateusz Sypniewski, Maria Śmigielska, Igor Kamiński, Emilia Wiśnios, Hanna Schreiber, Bartosz Pieliński | Cultural heritage is an arena of international relations that interests all states worldwide. The inscription process on the UNESCO World Heritage List and the UNESCO Representative List of the Intangible Cultural Heritage of Humanity often leads to tensions and conflicts among states. This research addresses these cha... | http://arxiv.org/abs/2307.16573v2 | 2023-07-31T11:06:08Z | cs.CL | 2,023 |
Unveiling Security, Privacy, and Ethical Concerns of ChatGPT | Xiaodong Wu, Ran Duan, Jianbing Ni | This paper delves into the realm of ChatGPT, an AI-powered chatbot that utilizes topic modeling and reinforcement learning to generate natural responses. Although ChatGPT holds immense promise across various industries, such as customer service, education, mental health treatment, personal productivity, and content cre... | http://arxiv.org/abs/2307.14192v1 | 2023-07-26T13:45:18Z | cs.CR, cs.AI | 2,023 |
Towards Generalising Neural Topical Representations | Xiaohao Yang, He Zhao, Dinh Phung, Lan Du | Topic models have evolved from conventional Bayesian probabilistic models to recent Neural Topic Models (NTMs). Although NTMs have shown promising performance when trained and tested on a specific corpus, their generalisation ability across corpora has yet to be studied. In practice, we often expect that an NTM trained... | http://arxiv.org/abs/2307.12564v2 | 2023-07-24T07:17:33Z | cs.CL, cs.LG | 2,023 |
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition | Yuzhao Mao, Di Lu, Xiaojie Wang, Yang Zhang | This paper concentrates on the understanding of interlocutors' emotions evoked in conversational utterances. Previous studies in this literature mainly focus on more accurate emotional predictions, while ignoring model robustness when the local context is corrupted by adversarial attacks. To maintain robustness while e... | http://arxiv.org/abs/2307.12221v1 | 2023-07-23T04:01:24Z | cs.CL, cs.AI | 2,023 |
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources | Jiasheng Si, Yingjie Zhu, Xingyu Shi, Deyu Zhou, Yulan He | Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two iss... | http://arxiv.org/abs/2307.12131v1 | 2023-07-22T17:26:55Z | cs.CL | 2,023 |
Random Separating Hyperplane Theorem and Learning Polytopes | Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar | The Separating Hyperplane theorem is a fundamental result in Convex Geometry with myriad applications. Our first result, Random Separating Hyperplane Theorem (RSH), is a strengthening of this for polytopes. $\rsh$ asserts that if the distance between $a$ and a polytope $K$ with $k$ vertices and unit diameter in $\Re^d$... | http://arxiv.org/abs/2307.11371v1 | 2023-07-21T06:03:43Z | cs.LG, cs.CG | 2,023 |
What Twitter Data Tell Us about the Future? | Alina Landowska, Marek Robak, Maciej Skorski | Anticipation is a fundamental human cognitive ability that involves thinking about and living towards the future. While language markers reflect anticipatory thinking, research on anticipation from the perspective of natural language processing is limited. This study aims to investigate the futures projected by futuris... | http://arxiv.org/abs/2308.02035v1 | 2023-07-20T14:02:47Z | cs.CY, cs.CL, cs.LG, cs.SI | 2,023 |
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization | Daniel Atzberger, Tim Cech, Willy Scheibel, Matthias Trapp, Rico Richter, Jürgen Döllner, Tobias Schreck | Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting semantic similarity b... | http://arxiv.org/abs/2307.11770v1 | 2023-07-17T14:08:25Z | cs.CL, cs.LG | 2,023 |
Measuring Online Emotional Reactions to Events | Siyi Guo, Zihao He, Ashwin Rao, Eugene Jang, Yuanfeixue Nan, Fred Morstatter, Jeffrey Brantingham, Kristina Lerman | The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using this data to understand social behavior is difficult due heterogeneity of topics and events discussed in the highly dy... | http://arxiv.org/abs/2307.10245v2 | 2023-07-17T06:52:30Z | cs.SI, physics.soc-ph | 2,023 |
A Topical Approach to Capturing Customer Insight In Social Media | Miguel Palencia-Olivar | The age of social media has opened new opportunities for businesses. This flourishing wealth of information is outside traditional channels and frameworks of classical marketing research, including that of Marketing Mix Modeling (MMM). Textual data, in particular, poses many challenges that data analysis practitioners ... | http://arxiv.org/abs/2307.11775v1 | 2023-07-14T11:15:28Z | cs.CL, cs.LG, stat.ML | 2,023 |
Digital Health Discussion Through Articles Published Until the Year 2021: A Digital Topic Modeling Approach | Junhyoun Sung, Hyungsook Kim | The digital health industry has grown in popularity since the 2010s, but there has been limited analysis of the topics discussed in the field across academic disciplines. This study aims to analyze the research trends of digital health-related articles published on the Web of Science until 2021, in order to understand ... | http://arxiv.org/abs/2307.07130v2 | 2023-07-14T02:55:39Z | stat.AP, cs.IR | 2,023 |
Detecting the Presence of COVID-19 Vaccination Hesitancy from South African Twitter Data Using Machine Learning | Nicholas Perikli, Srimoy Bhattacharya, Blessing Ogbuokiri, Zahra Movahedi Nia, Benjamin Lieberman, Nidhi Tripathi, Salah-Eddine Dahbi, Finn Stevenson, Nicola Bragazzi, Jude Kong, Bruce Mellado | Very few social media studies have been done on South African user-generated content during the COVID-19 pandemic and even fewer using hand-labelling over automated methods. Vaccination is a major tool in the fight against the pandemic, but vaccine hesitancy jeopardizes any public health effort. In this study, sentimen... | http://arxiv.org/abs/2307.15072v1 | 2023-07-12T13:28:37Z | cs.CY, cs.CL, cs.LG, cs.SI | 2,023 |
S2vNTM: Semi-supervised vMF Neural Topic Modeling | Weijie Xu, Jay Desai, Srinivasan Sengamedu, Xiaoyu Jiang, Francis Iannacci | Language model based methods are powerful techniques for text classification. However, the models have several shortcomings. (1) It is difficult to integrate human knowledge such as keywords. (2) It needs a lot of resources to train the models. (3) It relied on large text data to pretrain. In this paper, we propose Sem... | http://arxiv.org/abs/2307.04804v2 | 2023-07-06T21:44:31Z | cs.CL, cs.AI, 68T50, I.2.7 | 2,023 |
Graph Contrastive Topic Model | Zheheng Luo, Lei Liu, Qianqian Xie, Sophia Ananiadou | Existing NTMs with contrastive learning suffer from the sample bias problem owing to the word frequency-based sampling strategy, which may result in false negative samples with similar semantics to the prototypes. In this paper, we aim to explore the efficient sampling strategy and contrastive learning in NTMs to addre... | http://arxiv.org/abs/2307.02078v1 | 2023-07-05T07:39:47Z | cs.CL | 2,023 |
KDSTM: Neural Semi-supervised Topic Modeling with Knowledge Distillation | Weijie Xu, Xiaoyu Jiang, Jay Desai, Bin Han, Fuqin Yan, Francis Iannacci | In text classification tasks, fine tuning pretrained language models like BERT and GPT-3 yields competitive accuracy; however, both methods require pretraining on large text datasets. In contrast, general topic modeling methods possess the advantage of analyzing documents to extract meaningful patterns of words without... | http://arxiv.org/abs/2307.01878v2 | 2023-07-04T18:49:19Z | cs.CL, cs.AI, 68T50, I.2.6 | 2,023 |
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport | Weijie Xu, Xiaoyu Jiang, Srinivasan H. Sengamedu, Francis Iannacci, Jinjin Zhao | Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge. This work presents a semi-supervised neural topic modeling method, vONTSS, which... | http://arxiv.org/abs/2307.01226v2 | 2023-07-03T04:23:41Z | cs.LG, cs.AI, cs.CL, cs.IT, math.IT | 2,023 |
TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching | Khang Truong Giang, Soohwan Song, Sungho Jo | This study tackles the challenge of image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these app... | http://arxiv.org/abs/2307.00485v1 | 2023-07-02T06:14:07Z | cs.CV | 2,023 |
Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations | Ratanond Koonchanok, Yanling Pan, Hyeju Jang | ChatGPT sets a new record with the fastest-growing user base, as a chatbot powered by a large language model (LLM). While it demonstrates state-of-the-art capabilities in a variety of language-generation tasks, it also raises widespread public concerns regarding its societal impact. In this paper, we investigated publi... | http://arxiv.org/abs/2306.12951v2 | 2023-06-22T15:10:18Z | cs.CL | 2,023 |
Concept-Based Visual Analysis of Dynamic Textual Data | Xiang Shouxing, Ouyang Fangxin, Liu Shixia | Analyzing how interrelated ideas flow within and between multiple social groups helps understand the propagation of information, ideas, and thoughts on social media. The existing dynamic text analysis work on idea flow analysis is mostly based on the topic model. Therefore, when analyzing the reasons behind the flow of... | http://arxiv.org/abs/2306.10462v1 | 2023-06-18T03:21:32Z | cs.HC | 2,023 |
A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews | Robert Lakatos, Gergo Bogacsovics, Balazs Harangi, Istvan Lakatos, Attila Tiba, Janos Toth, Marianna Szabo, Andras Hajdu | The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. In this paper, we present a cloud-based system that can extract insigh... | http://arxiv.org/abs/2306.07786v2 | 2023-06-13T14:07:52Z | cs.CL, cs.AI | 2,023 |
Topic-Centric Explanations for News Recommendation | Dairui Liu, Derek Greene, Irene Li, Xuefei Jiang, Ruihai Dong | News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests. Recent methods have demonstrated considerable success in terms of recommendation performance. However, the lack of explanation for these recommendations can lead to mistrust am... | http://arxiv.org/abs/2306.07506v2 | 2023-06-13T02:33:27Z | cs.IR | 2,023 |
Causality between Sentiment and Cryptocurrency Prices | Lubdhak Mondal, Udeshya Raj, Abinandhan S, Began Gowsik S, Sarwesh P, Abhijeet Chandra | This study investigates the relationship between narratives conveyed through microblogging platforms, namely Twitter, and the value of crypto assets. Our study provides a unique technique to build narratives about cryptocurrency by combining topic modelling of short texts with sentiment analysis. First, we used an unsu... | http://arxiv.org/abs/2306.05803v1 | 2023-06-09T10:40:22Z | q-fin.CP, cs.CL, cs.LG, I.2.7 | 2,023 |
A modified model for topic detection from a corpus and a new metric evaluating the understandability of topics | Tomoya Kitano, Yuto Miyatake, Daisuke Furihata | This paper presents a modified neural model for topic detection from a corpus and proposes a new metric to evaluate the detected topics. The new model builds upon the embedded topic model incorporating some modifications such as document clustering. Numerical experiments suggest that the new model performs favourably r... | http://arxiv.org/abs/2306.04941v1 | 2023-06-08T05:17:03Z | cs.CL, cs.LG | 2,023 |
Effective Neural Topic Modeling with Embedding Clustering Regularization | Xiaobao Wu, Xinshuai Dong, Thong Nguyen, Anh Tuan Luu | Topic models have been prevalent for decades with various applications. However, existing topic models commonly suffer from the notorious topic collapsing: discovered topics semantically collapse towards each other, leading to highly repetitive topics, insufficient topic discovery, and damaged model interpretability. I... | http://arxiv.org/abs/2306.04217v1 | 2023-06-07T07:45:38Z | cs.CL | 2,023 |
Reconstructing human activities via coupling mobile phone data with location-based social networks | Le Huang, Fan Xia, Hui Chen, Bowen Hu, Xiao Zhou, Chunxiao Li, Yaohui Jin, Yanyan Xu | In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants' behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the... | http://arxiv.org/abs/2306.03441v1 | 2023-06-06T06:37:14Z | cs.SI, cs.CY | 2,023 |
Literature-based Discovery for Landscape Planning | David Marasco, Ilya Tyagin, Justin Sybrandt, James H. Spencer, Ilya Safro | This project demonstrates how medical corpus hypothesis generation, a knowledge discovery field of AI, can be used to derive new research angles for landscape and urban planners. The hypothesis generation approach herein consists of a combination of deep learning with topic modeling, a probabilistic approach to natural... | http://arxiv.org/abs/2306.02588v1 | 2023-06-05T04:32:46Z | cs.AI | 2,023 |
ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives | Hamed Rahimi, Hubert Naacke, Camelia Constantin, Bernd Amann | This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence f... | http://arxiv.org/abs/2306.02221v1 | 2023-06-04T00:32:45Z | cs.IR, cs.AI | 2,023 |
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews | Yukyung Lee, Jaehee Kim, Doyoon Kim, Yookyung Kho, Younsun Kim, Pilsung Kang | As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis mode... | http://arxiv.org/abs/2306.02043v1 | 2023-06-03T07:51:57Z | cs.AI | 2,023 |
Leveraging Natural Language Processing For Public Health Screening On YouTube: A COVID-19 Case Study | Ahrar Bin Aslam, Zafi Sherhan Syed, Muhammad Faiz Khan, Asghar Baloch, Muhammad Shehram Shah Syed | Background: Social media platforms have become a viable source of medical information, with patients and healthcare professionals using them to share health-related information and track diseases. Similarly, YouTube, the largest video-sharing platform in the world contains vlogs where individuals talk about their illne... | http://arxiv.org/abs/2306.01164v1 | 2023-06-01T21:40:48Z | cs.CL, cs.SI | 2,023 |
Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure | Ankita Agarwal, Tanvi Banerjee, William L. Romine, Krishnaprasad Thirunarayan, Lingwei Chen, Mia Cajita | Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and ... | http://arxiv.org/abs/2305.19373v1 | 2023-05-30T19:30:40Z | cs.LG, cs.AI, cs.CL | 2,023 |
Research on Multilingual News Clustering Based on Cross-Language Word Embeddings | Lin Wu, Rui Li, Wong-Hing Lam | Classifying the same event reported by different countries is of significant importance for public opinion control and intelligence gathering. Due to the diverse types of news, relying solely on transla-tors would be costly and inefficient, while depending solely on translation systems would incur considerable performa... | http://arxiv.org/abs/2305.18880v1 | 2023-05-30T09:24:55Z | cs.CL | 2,023 |
The Effects of Political Martyrdom on Election Results: The Assassination of Abe | Miu Nicole Takagi | In developed nations assassinations are rare and thus the impact of such acts on the electoral and political landscape is understudied. In this paper, we focus on Twitter data to examine the effects of Japan's former Primer Minister Abe's assassination on the Japanese House of Councillors elections in 2022. We utilize ... | http://arxiv.org/abs/2305.18004v2 | 2023-05-29T10:33:08Z | cs.CL | 2,023 |
Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring | Heejin Do, Yunsu Kim, Gary Geunbae Lee | Automated essay scoring (AES) aims to score essays written for a given prompt, which defines the writing topic. Most existing AES systems assume to grade essays of the same prompt as used in training and assign only a holistic score. However, such settings conflict with real-education situations; pre-graded essays for ... | http://arxiv.org/abs/2305.16826v1 | 2023-05-26T11:11:19Z | cs.CL, cs.AI | 2,023 |
Diversity-Aware Coherence Loss for Improving Neural Topic Models | Raymond Li, Felipe González-Pizarro, Linzi Xing, Gabriel Murray, Giuseppe Carenini | The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss. Since neural topic models are trained by recreating individual input documents, they do not explicitl... | http://arxiv.org/abs/2305.16199v2 | 2023-05-25T16:01:56Z | cs.CL, cs.LG | 2,023 |
Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation | Hao Li, Viktor Schlegel, Riza Batista-Navarro, Goran Nenadic | Argument summarisation is a promising but currently under-explored field. Recent work has aimed to provide textual summaries in the form of concise and salient short texts, i.e., key points (KPs), in a task known as Key Point Analysis (KPA). One of the main challenges in KPA is finding high-quality key point candidates... | http://arxiv.org/abs/2305.16000v1 | 2023-05-25T12:43:29Z | cs.CL, cs.AI | 2,023 |
Topic-Guided Self-Introduction Generation for Social Media Users | Chunpu Xu, Jing Li, Piji Li, Min Yang | Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests. While most prior work profiles users with tags (e.g., ages), we investigate senten... | http://arxiv.org/abs/2305.15138v1 | 2023-05-24T13:35:08Z | cs.CL, cs.AI, cs.LG | 2,023 |
A Survey of Diffusion Models in Natural Language Processing | Hao Zou, Zae Myung Kim, Dongyeop Kang | This survey paper provides a comprehensive review of the use of diffusion models in natural language processing (NLP). Diffusion models are a class of mathematical models that aim to capture the diffusion of information or signals across a network or manifold. In NLP, diffusion models have been used in a variety of app... | http://arxiv.org/abs/2305.14671v2 | 2023-05-24T03:25:32Z | cs.CL | 2,023 |
Contextualized Topic Coherence Metrics | Hamed Rahimi, Jacob Louis Hoover, David Mimno, Hubert Naacke, Camelia Constantin, Bernd Amann | The recent explosion in work on neural topic modeling has been criticized for optimizing automated topic evaluation metrics at the expense of actual meaningful topic identification. But human annotation remains expensive and time-consuming. We propose LLM-based methods inspired by standard human topic evaluations, in a... | http://arxiv.org/abs/2305.14587v1 | 2023-05-23T23:53:29Z | cs.CL, cs.IR | 2,023 |
Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person | Lucas Rafael Stefanel Gris, Ricardo Marcacini, Arnaldo Candido Junior, Edresson Casanova, Anderson Soares, Sandra Maria Aluísio | Automatic speech recognition (ASR) systems play a key role in applications involving human-machine interactions. Despite their importance, ASR models for the Portuguese language proposed in the last decade have limitations in relation to the correct identification of punctuation marks in automatic transcriptions, which... | http://arxiv.org/abs/2305.14580v2 | 2023-05-23T23:37:29Z | cs.CL, cs.AI | 2,023 |
Revisiting Automated Topic Model Evaluation with Large Language Models | Dominik Stammbach, Vilém Zouhar, Alexander Hoyle, Mrinmaya Sachan, Elliott Ash | Topic models are used to make sense of large text collections. However, automatically evaluating topic model output and determining the optimal number of topics both have been longstanding challenges, with no effective automated solutions to date. This paper proposes using large language models to evaluate such output.... | http://arxiv.org/abs/2305.12152v2 | 2023-05-20T09:42:00Z | cs.CL | 2,023 |
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling | Shengqiong Wu, Hao Fei, Yixin Cao, Lidong Bing, Tat-Seng Chua | Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. To combat that, we propose a novel framework that simultaneously implements the idea of internal-information screening and external-information e... | http://arxiv.org/abs/2305.11719v2 | 2023-05-19T14:56:57Z | cs.CV, cs.CL | 2,023 |
Large-Scale Text Analysis Using Generative Language Models: A Case Study in Discovering Public Value Expressions in AI Patents | Sergio Pelaez, Gaurav Verma, Barbara Ribeiro, Philip Shapira | Labeling data is essential for training text classifiers but is often difficult to accomplish accurately, especially for complex and abstract concepts. Seeking an improved method, this paper employs a novel approach using a generative language model (GPT-4) to produce labels and rationales for large-scale text analysis... | http://arxiv.org/abs/2305.10383v2 | 2023-05-17T17:18:26Z | cs.CL, cs.IR | 2,023 |
Constructing and Interpreting Causal Knowledge Graphs from News | Fiona Anting Tan, Debdeep Paul, Sahim Yamaura, Miura Koji, See-Kiong Ng | Many financial jobs rely on news to learn about causal events in the past and present, to make informed decisions and predictions about the future. With the ever-increasing amount of news available online, there is a need to automate the extraction of causal events from unstructured texts. In this work, we propose a me... | http://arxiv.org/abs/2305.09359v2 | 2023-05-16T11:33:32Z | cs.CL | 2,023 |
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling | Zheng Fang, Yulan He, Rob Procter | Most existing topic models rely on bag-of-words (BOW) representation, which limits their ability to capture word order information and leads to challenges with out-of-vocabulary (OOV) words in new documents. Contextualized word embeddings, however, show superiority in word sense disambiguation and effectively address t... | http://arxiv.org/abs/2305.09329v3 | 2023-05-16T10:07:33Z | cs.CL, cs.AI | 2,023 |
HyHTM: Hyperbolic Geometry based Hierarchical Topic Models | Simra Shahid, Tanay Anand, Nikitha Srikanth, Sumit Bhatia, Balaji Krishnamurthy, Nikaash Puri | Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lowerlevel topics are unrelated and not specific enough to their higher-level topics. Additionally, these methods can be computationally expensive. We pre... | http://arxiv.org/abs/2305.09258v1 | 2023-05-16T08:06:11Z | cs.IR, cs.CL | 2,023 |
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections | Maria Leonor Pacheco, Tunazzina Islam, Lyle Ungar, Ming Yin, Dan Goldwasser | Experts across diverse disciplines are often interested in making sense of large text collections. Traditionally, this challenge is approached either by noisy unsupervised techniques such as topic models, or by following a manual theme discovery process. In this paper, we expand the definition of a theme to account for... | http://arxiv.org/abs/2305.05094v1 | 2023-05-08T23:43:15Z | cs.CL, cs.HC | 2,023 |
Reinforcement Learning for Topic Models | Jeremy Costello, Marek Z. Reformat | We apply reinforcement learning techniques to topic modeling by replacing the variational autoencoder in ProdLDA with a continuous action space reinforcement learning policy. We train the system with a policy gradient algorithm REINFORCE. Additionally, we introduced several modifications: modernize the neural network a... | http://arxiv.org/abs/2305.04843v1 | 2023-05-08T16:41:08Z | cs.CL, cs.LG | 2,023 |
Two to Five Truths in Non-Negative Matrix Factorization | John M. Conroy, Neil P Molino, Brian Baughman, Rod Gomez, Ryan Kaliszewski, Nicholas A. Lines | In this paper, we explore the role of matrix scaling on a matrix of counts when building a topic model using non-negative matrix factorization. We present a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly improve the quality of a non-negative matrix factorization. The results parallel thos... | http://arxiv.org/abs/2305.05389v2 | 2023-05-06T14:40:20Z | cs.LG | 2,023 |
Can LLMs Capture Human Preferences? | Ali Goli, Amandeep Singh | We explore the viability of Large Language Models (LLMs), specifically OpenAI's GPT-3.5 and GPT-4, in emulating human survey respondents and eliciting preferences, with a focus on intertemporal choices. Leveraging the extensive literature on intertemporal discounting for benchmarking, we examine responses from LLMs acr... | http://arxiv.org/abs/2305.02531v6 | 2023-05-04T03:51:31Z | cs.CL, cs.AI | 2,023 |
Natural language processing on customer note data | Andrew Hilditch, David Webb, Jozef Baca, Tom Armitage, Matthew Shardlow, Peter Appleby | Automatic analysis of customer data for businesses is an area that is of interest to companies. Business to business data is studied rarely in academia due to the sensitive nature of such information. Applying natural language processing can speed up the analysis of prohibitively large sets of data. This paper addresse... | http://arxiv.org/abs/2305.02029v1 | 2023-05-03T10:36:56Z | cs.CL | 2,023 |
tmfast fits topic models fast | Daniel J. Hicks | tmfast is an R package for fitting topic models using a fast algorithm based on partial PCA and the varimax rotation. After providing mathematical background to the method, we present two examples, using a simulated corpus and aggregated works of a selection of authors from the long nineteenth century, and compare the ... | http://arxiv.org/abs/2305.01535v1 | 2023-05-02T15:43:59Z | stat.ME, stat.CO | 2,023 |
Insights into Software Development Approaches: Mining Q&A Repositories | Arif Ali Khan, Javed Ali Khan, Muhammad Azeem Akbar, Peng Zhou, Mahdi Fahmideh | Context: Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners discussions on Q&A forums. Objective: We conducted an empirical study to analyze de... | http://arxiv.org/abs/2305.01315v1 | 2023-05-02T10:51:21Z | cs.SE | 2,023 |
ChatGPT in education: A discourse analysis of worries and concerns on social media | Lingyao Li, Zihui Ma, Lizhou Fan, Sanggyu Lee, Huizi Yu, Libby Hemphill | The rapid advancements in generative AI models present new opportunities in the education sector. However, it is imperative to acknowledge and address the potential risks and concerns that may arise with their use. We analyzed Twitter data to identify key concerns related to the use of ChatGPT in education. We employed... | http://arxiv.org/abs/2305.02201v1 | 2023-04-29T22:08:42Z | cs.CY | 2,023 |
Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework | David Alonso del Barrio, Daniel Gatica-Perez | This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine movement over a period of 22 months in 2020-2021, we used Natura... | http://arxiv.org/abs/2305.00182v1 | 2023-04-29T06:26:03Z | cs.CL | 2,023 |
pyBibX -- A Python Library for Bibliometric and Scientometric Analysis Powered with Artificial Intelligence Tools | Valdecy Pereira, Marcio Pereira Basilio, Carlos Henrique Tarjano Santos | Bibliometric and Scientometric analyses offer invaluable perspectives on the complex research terrain and collaborative dynamics spanning diverse academic disciplines. This paper presents pyBibX, a python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Sco... | http://arxiv.org/abs/2304.14516v1 | 2023-04-27T20:06:07Z | cs.DL, cs.AI | 2,023 |
On the Identification of the Energy related Issues from the App Reviews | Noshin Nawal | The energy inefficiency of the apps can be a major issue for the app users which is discussed on App Stores extensively. Previous research has shown the importance of investigating the energy related app reviews to identify the major causes or categories of energy related user feedback. However, there is no study that ... | http://arxiv.org/abs/2304.11292v1 | 2023-04-22T01:54:30Z | cs.AI, cs.CL, cs.LG | 2,023 |
Word Sense Induction with Knowledge Distillation from BERT | Anik Saha, Alex Gittens, Bulent Yener | Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embeddings are an efficient alternative in these settings. Such methods typically use one vector to encode multiple different meanings of a word, and... | http://arxiv.org/abs/2304.10642v1 | 2023-04-20T21:05:35Z | cs.CL | 2,023 |
Political corpus creation through automatic speech recognition on EU debates | Hugo de Vos, Suzan Verberne | In this paper, we present a transcribed corpus of the LIBE committee of the EU parliament, totalling 3.6 Million running words. The meetings of parliamentary committees of the EU are a potentially valuable source of information for political scientists but the data is not readily available because only disclosed as spe... | http://arxiv.org/abs/2304.08137v1 | 2023-04-17T10:41:59Z | cs.CL | 2,023 |
The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges | Rémi Boutin, Pierre Latouche, Charles Bouveyron | Numerical interactions leading to users sharing textual content published by others are naturally represented by a network where the individuals are associated with the nodes and the exchanged texts with the edges. To understand those heterogeneous and complex data structures, clustering nodes into homogeneous groups a... | http://arxiv.org/abs/2304.08242v3 | 2023-04-14T07:01:57Z | cs.LG, cs.CL, cs.SI, stat.ME | 2,023 |
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