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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The Increased Effect of Elections and Changing Prime Ministers on Topics Discussed in the Australian Federal Parliament between 1901 and 2018 | Rohan Alexander, Monica Alexander | Politics and discussion in parliament is likely to be influenced by the party in power and associated election cycles. However, little is known about the extent to which these events affect discussion and how this has changed over time. We systematically analyse how discussion in the Australian Federal Parliament chang... | http://arxiv.org/abs/2111.09299v1 | 2021-11-17T18:55:07Z | stat.AP | 2,021 |
Community-Detection via Hashtag-Graphs for Semi-Supervised NMF Topic Models | Mattias Luber, Anton Thielmann, Christoph Weisser, Benjamin Säfken | Extracting topics from large collections of unstructured text-documents has become a central task in current NLP applications and algorithms like NMF, LDA as well as their generalizations are the well-established current state of the art. However, especially when it comes to short text documents like Tweets, these appr... | http://arxiv.org/abs/2111.10401v1 | 2021-11-17T12:52:16Z | cs.SI, cs.LG | 2,021 |
Utilizing Textual Reviews in Latent Factor Models for Recommender Systems | Tatev Karen Aslanyan, Flavius Frasincar | Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thou... | http://arxiv.org/abs/2111.08538v1 | 2021-11-16T15:07:51Z | cs.IR, cs.LG, stat.ML | 2,021 |
Regional Topics in British Grocery Retail Transactions | Mariflor Vega Carrasco, Mirco Musolesi, Jason O'Sullivan, Rosie Prior, Ioanna Manolopoulou | Understanding the customer behaviours behind transactional data has high commercial value in the grocery retail industry. Customers generate millions of transactions every day, choosing and buying products to satisfy specific shopping needs. Product availability may vary geographically due to local demand and local sup... | http://arxiv.org/abs/2111.08078v1 | 2021-11-15T20:55:53Z | stat.AP, stat.ME | 2,021 |
Forecasting Crude Oil Price Using Event Extraction | Jiangwei Liu, Xiaohong Huang | Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and poli... | http://arxiv.org/abs/2111.09111v1 | 2021-11-14T08:48:43Z | cs.LG, cs.AI, cs.CL, econ.GN, q-fin.EC, stat.AP | 2,021 |
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation | Yu Zhang, Wei Wei, Binxuan Huang, Kathleen M. Carley, Yan Zhang | Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection. While tweet text is the most commonly used feature in location estimation, most of the prior works suffer from either the noise or the sparsity of textual features. In this pa... | http://arxiv.org/abs/2111.06515v1 | 2021-11-12T00:57:42Z | cs.CL, cs.LG | 2,021 |
A quantitative and qualitative open citation analysis of retracted articles in the humanities | Ivan Heibi, Silvio Peroni | In this article, we show and discuss the results of a quantitative and qualitative analysis of open citations to retracted publications in the humanities domain. Our study was conducted by selecting retracted papers in the humanities domain and marking their main characteristics (e.g., retraction reason). Then, we gath... | http://arxiv.org/abs/2111.05223v3 | 2021-11-09T16:02:16Z | cs.DL, cs.IR | 2,021 |
Trend and Thoughts: Understanding Climate Change Concern using Machine Learning and Social Media Data | Zhongkai Shangguan, Zihe Zheng, Lei Lin | Nowadays social media platforms such as Twitter provide a great opportunity to understand public opinion of climate change compared to traditional survey methods. In this paper, we constructed a massive climate change Twitter dataset and conducted comprehensive analysis using machine learning. By conducting topic model... | http://arxiv.org/abs/2111.14929v1 | 2021-11-06T19:59:03Z | cs.CL | 2,021 |
Investigation of Topic Modelling Methods for Understanding the Reports of the Mining Projects in Queensland | Yasuko Okamoto, Thirunavukarasu Balasubramaniam, Richi Nayak | In the mining industry, many reports are generated in the project management process. These past documents are a great resource of knowledge for future success. However, it would be a tedious and challenging task to retrieve the necessary information if the documents are unorganized and unstructured. Document clusterin... | http://arxiv.org/abs/2111.03576v1 | 2021-11-05T15:52:03Z | cs.IR, cs.LG | 2,021 |
A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining | Gerhard Johann Hagerer, Wing Sheung Leung, Qiaoxi Liu, Hannah Danner, Georg Groh | User-generated content from social media is produced in many languages, making it technically challenging to compare the discussed themes from one domain across different cultures and regions. It is relevant for domains in a globalized world, such as market research, where people from two nations and markets might have... | http://arxiv.org/abs/2111.02259v3 | 2021-11-03T14:49:50Z | cs.CL, cs.IR | 2,021 |
Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index | Hairo U. Miranda Belmonte, Victor Muñiz-Sánchez, Francisco Corona | Quantification of economic uncertainty is a key concept for the prediction of macro economic variables such as gross domestic product (GDP), and it becomes particularly relevant on real-time or short-time predictions methodologies, such as nowcasting, where it is required a large amount of time series data, commonly wi... | http://arxiv.org/abs/2111.00057v1 | 2021-10-29T19:31:03Z | cs.LG, cs.IR | 2,021 |
Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities | Simmi Marina Joseph, Salvatore Citraro, Virginia Morini, Giulio Rossetti, Massimo Stella | Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between conceptsin texts as co-occurrences ... | http://arxiv.org/abs/2110.15269v2 | 2021-10-28T16:26:50Z | cs.CL, cs.CY | 2,021 |
TopicNet: Semantic Graph-Guided Topic Discovery | Zhibin Duan, Yishi Xu, Bo Chen, Dongsheng Wang, Chaojie Wang, Mingyuan Zhou | Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic hierarchy. However, it is unclear how to incorporate prior beliefs such as knowledge graph to guide the learning of the topic hierarchy. To... | http://arxiv.org/abs/2110.14286v1 | 2021-10-27T09:07:14Z | cs.LG, cs.IR | 2,021 |
Contrastive Learning for Neural Topic Model | Thong Nguyen, Anh Tuan Luu | Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by differentiating a document with another dissimilar sample. However, utilizing that discriminative-generative architecture has two important drawbacks: (1) the architecture does not relate simi... | http://arxiv.org/abs/2110.12764v1 | 2021-10-25T09:46:26Z | cs.CL | 2,021 |
Recommender Systems meet Mechanism Design | Yang Cai, Constantinos Daskalakis | Machine learning has developed a variety of tools for learning and representing high-dimensional distributions with structure. Recent years have also seen big advances in designing multi-item mechanisms. Akin to overfitting, however, these mechanisms can be extremely sensitive to the Bayesian prior that they target, wh... | http://arxiv.org/abs/2110.12558v2 | 2021-10-25T00:03:30Z | cs.GT, cs.IR, cs.LG, stat.ML | 2,021 |
Topic-Guided Abstractive Multi-Document Summarization | Peng Cui, Le Hu | A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-s... | http://arxiv.org/abs/2110.11207v1 | 2021-10-21T15:32:30Z | cs.CL, cs.AI | 2,021 |
SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural Topic Models on Social Media Opinion Mining | Gerhard Johann Hagerer, Martin Kirchhoff, Hannah Danner, Robert Pesch, Mainak Ghosh, Archishman Roy, Jiaxi Zhao, Georg Groh | Recent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive... | http://arxiv.org/abs/2110.10575v2 | 2021-10-20T14:04:13Z | cs.CL | 2,021 |
Uncertainty-aware Topic Modeling Visualization | Valerie Müller, Christian Sieg, Lars Linsen | Topic modeling is a state-of-the-art technique for analyzing text corpora. It uses a statistical model, most commonly Latent Dirichlet Allocation (LDA), to discover abstract topics that occur in the document collection. However, the LDA-based topic modeling procedure is based on a randomly selected initial configuratio... | http://arxiv.org/abs/2110.09247v1 | 2021-10-18T12:48:33Z | cs.HC | 2,021 |
n-stage Latent Dirichlet Allocation: A Novel Approach for LDA | Zekeriya Anil Guven, Banu Diri, Tolgahan Cakaloglu | Nowadays, data analysis has become a problem as the amount of data is constantly increasing. In order to overcome this problem in textual data, many models and methods are used in natural language processing. The topic modeling field is one of these methods. Topic modeling allows determining the semantic structure of a... | http://arxiv.org/abs/2110.08591v2 | 2021-10-16T15:26:53Z | cs.CL, cs.IR, H.3.3; I.2.7; I.7.0 | 2,021 |
Neural Attention-Aware Hierarchical Topic Model | Yuan Jin, He Zhao, Ming Liu, Lan Du, Wray Buntine | Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count information is utilized for the training, while more fine-grained sentence-level information is ignored, and (2) external semantic knowledge re... | http://arxiv.org/abs/2110.07161v1 | 2021-10-14T05:42:32Z | cs.CL, cs.LG | 2,021 |
Topic Model Supervised by Understanding Map | Gangli Liu | Inspired by the notion of Center of Mass in physics, an extension called Semantic Center of Mass (SCOM) is proposed, and used to discover the abstract "topic" of a document. The notion is under a framework model called Understanding Map Supervised Topic Model (UM-S-TM). The devising aim of UM-S-TM is to let both the do... | http://arxiv.org/abs/2110.06043v12 | 2021-10-12T14:42:33Z | cs.CL | 2,021 |
Topic Modeling, Clade-assisted Sentiment Analysis, and Vaccine Brand Reputation Analysis of COVID-19 Vaccine-related Facebook Comments in the Philippines | Jasper Kyle Catapang, Jerome V. Cleofas | Vaccine hesitancy and other COVID-19-related concerns and complaints in the Philippines are evident on social media. It is important to identify these different topics and sentiments in order to gauge public opinion, use the insights to develop policies, and make necessary adjustments or actions to improve public image... | http://arxiv.org/abs/2111.04416v1 | 2021-10-11T11:08:38Z | cs.CY, cs.CL | 2,021 |
Hotel Preference Rank based on Online Customer Review | Muhammad Apriandito Arya Saputra, Andry Alamsyah, Fajar Ibnu Fatihan | Topline hotels are now shifting into the digital way in how they understand their customers to maintain and ensuring satisfaction. Rather than the conventional way which uses written reviews or interviews, the hotel is now heavily investing in Artificial Intelligence particularly Machine Learning solutions. Analysis of... | http://arxiv.org/abs/2110.06133v1 | 2021-10-10T15:59:01Z | cs.IR, cs.SI, econ.GN, q-fin.EC | 2,021 |
Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions | Hui Yin, Xiangyu Song, Shuiqiao Yang, Jianxin Li | The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world. Even worse, the emergence of the COVID-19 Delta variant once again puts the world in danger. Fortunately, many countries and companies have started to d... | http://arxiv.org/abs/2111.04415v1 | 2021-10-08T23:30:17Z | cs.CY, cs.CL | 2,021 |
Learning Topic Models: Identifiability and Finite-Sample Analysis | Yinyin Chen, Shishuang He, Yun Yang, Feng Liang | Topic models provide a useful text-mining tool for learning, extracting, and discovering latent structures in large text corpora. Although a plethora of methods have been proposed for topic modeling, lacking in the literature is a formal theoretical investigation of the statistical identifiability and accuracy of laten... | http://arxiv.org/abs/2110.04232v2 | 2021-10-08T16:35:42Z | stat.ML, cs.IR, cs.LG, stat.ME | 2,021 |
Analysis of the influence of political polarization in the vaccination stance: the Brazilian COVID-19 scenario | Régis Ebeling, Carlos Abel Córdova Sáenz, Jeferson Nobre, Karin Becker | The outbreak of COVID-19 had a huge global impact, and non-scientific beliefs and political polarization have significantly influenced the population's behavior. In this context, COVID vaccines were made available in an unprecedented time, but a high level of hesitance has been observed that can undermine community imm... | http://arxiv.org/abs/2110.03382v1 | 2021-10-07T12:21:33Z | cs.SI | 2,021 |
Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits | Mansura A Khan, Khalil Muhammad, Barry Smyth, David Coyle | Food-choices and eating-habits directly contribute to our long-term health. This makes the food recommender system a potential tool to address the global crisis of obesity and malnutrition. Over the past decade, artificial-intelligence and medical researchers became more invested in researching tools that can guide and... | http://arxiv.org/abs/2110.07045v1 | 2021-10-05T10:56:02Z | cs.HC, cs.AI, cs.IR, cs.LG, 68U35 (Primary), 68T35 (Secondary), 68T50(Secondary), I.2.1 | 2,021 |
Extracting Major Topics of COVID-19 Related Tweets | Faezeh Azizi, Hamed Vahdat-Nejad, Hamideh Hajiabadi, Mohammad Hossein Khosravi | With the outbreak of the Covid-19 virus, the activity of users on Twitter has significantly increased. Some studies have investigated the hot topics of tweets in this period; however, little attention has been paid to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the... | http://arxiv.org/abs/2110.01876v1 | 2021-10-05T08:40:51Z | cs.SI, cs.IR, cs.LG | 2,021 |
Beyond Topics: Discovering Latent Healthcare Objectives from Event Sequences | Adrian Caruana, Madhushi Bandara, Daniel Catchpoole, Paul J Kennedy | A meaningful understanding of clinical protocols and patient pathways helps improve healthcare outcomes. Electronic health records (EHR) reflect real-world treatment behaviours that are used to enhance healthcare management but present challenges; protocols and pathways are often loosely defined and with elements frequ... | http://arxiv.org/abs/2110.01160v1 | 2021-10-04T02:52:14Z | cs.LG, cs.AI, cs.CL | 2,021 |
Artificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysis | Tahereh Saheb, Mohammad Dehghani | Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway towa... | http://arxiv.org/abs/2110.00828v1 | 2021-10-02T15:51:51Z | cs.AI | 2,021 |
A Generalized Hierarchical Nonnegative Tensor Decomposition | Joshua Vendrow, Jamie Haddock, Deanna Needell | Nonnegative matrix factorization (NMF) has found many applications including topic modeling and document analysis. Hierarchical NMF (HNMF) variants are able to learn topics at various levels of granularity and illustrate their hierarchical relationship. Recently, nonnegative tensor factorization (NTF) methods have been... | http://arxiv.org/abs/2109.14820v2 | 2021-09-30T03:00:41Z | cs.LG, stat.ML | 2,021 |
Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets | Zekeriya Anil Guven, Banu Diri, Tolgahan Cakaloglu | With the development of technology, the use of social media has become quite common. Analyzing comments on social media in areas such as media and advertising plays an important role today. For this reason, new and traditional natural language processing methods are used to detect the emotion of these shares. In this p... | http://arxiv.org/abs/2110.00418v1 | 2021-09-27T18:43:52Z | cs.CL, cs.IR, cs.LG, H.3.3; I.2.7; I.7.0 | 2,021 |
Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts | Raluca Alexandra Fetic, Mikkel Jordahn, Lucas Chaves Lima, Rasmus Arpe Fogh Egebæk, Martin Carsten Nielsen, Benjamin Biering, Lars Kai Hansen | For a multilingual podcast streaming service, it is critical to be able to deliver relevant content to all users independent of language. Podcast content relevance is conventionally determined using various metadata sources. However, with the increasing quality of speech recognition in many languages, utilizing automat... | http://arxiv.org/abs/2109.12306v1 | 2021-09-25T07:59:31Z | cs.IR, cs.LG | 2,021 |
A Unified Graph-Based Approach to Disinformation Detection using Contextual and Semantic Relations | Marius Paraschiv, Nikos Salamanos, Costas Iordanou, Nikolaos Laoutaris, Michael Sirivianos | As recent events have demonstrated, disinformation spread through social networks can have dire political, economic and social consequences. Detecting disinformation must inevitably rely on the structure of the network, on users particularities and on event occurrence patterns. We present a graph data structure, which ... | http://arxiv.org/abs/2109.11781v1 | 2021-09-24T07:23:59Z | cs.SI | 2,021 |
Enriching and Controlling Global Semantics for Text Summarization | Thong Nguyen, Anh Tuan Luu, Truc Lu, Tho Quan | Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we att... | http://arxiv.org/abs/2109.10616v1 | 2021-09-22T09:31:50Z | cs.CL | 2,021 |
Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron | A. Feder Cooper, Maria Antoniak, Christopher De Sa, Marilyn Migiel, David Mimno | We explore Boccaccio's Decameron to see how digital humanities tools can be used for tasks that have limited data in a language no longer in contemporary use: medieval Italian. We focus our analysis on the question: Do the different storytellers in the text exhibit distinct personalities? To answer this question, we cu... | http://arxiv.org/abs/2109.10506v1 | 2021-09-22T03:42:14Z | cs.CL, cs.LG | 2,021 |
Towards Explainable Scientific Venue Recommendations | Bastian Schäfermeier, Gerd Stumme, Tom Hanika | Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the cont... | http://arxiv.org/abs/2109.11343v1 | 2021-09-21T10:25:26Z | cs.IR, cs.AI | 2,021 |
Evolution of topics in central bank speech communication | Magnus Hansson | This paper studies the content of central bank speech communication from 1997 through 2020 and asks the following questions: (i) What global topics do central banks talk about? (ii) How do these topics evolve over time? I turn to natural language processing, and more specifically Dynamic Topic Models, to answer these q... | http://arxiv.org/abs/2109.10058v1 | 2021-09-21T09:57:18Z | econ.GN, q-fin.EC | 2,021 |
Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model | Elaine Zosa, Ravi Shekhar, Mladen Karan, Matthew Purver | Moderation of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. Our analysis shows that while comments that violate the moderation rules mostly share common linguistic and thematic ... | http://arxiv.org/abs/2109.10033v1 | 2021-09-21T08:57:17Z | cs.CL | 2,021 |
Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of SNOMED codes | Moumita Bhattacharya, Claudine Jurkovitz, Hagit Shatkay | Patients associated with multiple co-occurring health conditions often face aggravated complications and less favorable outcomes. Co-occurring conditions are especially prevalent among individuals suffering from kidney disease, an increasingly widespread condition affecting 13% of the general population in the US. This... | http://arxiv.org/abs/2109.09199v1 | 2021-09-19T19:34:21Z | cs.LG | 2,021 |
Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community | Maximilian Wich, Adrian Gorniak, Tobias Eder, Daniel Bartmann, Burak Enes Çakici, Georg Groh | Since traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not moderate users content. One popular platform relevant for the German hater community is Telegram for whi... | http://arxiv.org/abs/2109.07346v2 | 2021-09-15T14:58:46Z | cs.CL | 2,021 |
Semantics of European poetry is shaped by conservative forces: The relationship between poetic meter and meaning in accentual-syllabic verse | Artjoms Šeļa, Petr Plecháč, Alie Lassche | Recent advances in cultural analytics and large-scale computational studies of art, literature and film often show that long-term change in the features of artistic works happens gradually. These findings suggest that conservative forces that shape creative domains might be underestimated. To this end, we provide the f... | http://arxiv.org/abs/2109.07148v1 | 2021-09-15T08:20:01Z | cs.CL | 2,021 |
What are the attackers doing now? Automating cyber threat intelligence extraction from text on pace with the changing threat landscape: A survey | Md Rayhanur Rahman, Rezvan Mahdavi-Hezaveh, Laurie Williams | Cybersecurity researchers have contributed to the automated extraction of CTI from textual sources, such as threat reports and online articles, where cyberattack strategies, procedures, and tools are described. The goal of this article is to aid cybersecurity researchers understand the current techniques used for cyber... | http://arxiv.org/abs/2109.06808v1 | 2021-09-14T16:38:41Z | cs.CR, cs.CL, cs.LG | 2,021 |
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration | Shufan Wang, Laure Thompson, Mohit Iyyer | Phrase representations derived from BERT often do not exhibit complex phrasal compositionality, as the model relies instead on lexical similarity to determine semantic relatedness. In this paper, we propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings. Our approach (P... | http://arxiv.org/abs/2109.06304v2 | 2021-09-13T20:31:57Z | cs.CL | 2,021 |
Multiscale Analysis of Count Data through Topic Alignment | Julia Fukuyama, Kris Sankaran, Laura Symul | Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics $K$. Since there is no definitive way to choose $K$ and since a true value might not exist, we develop techniques to study the relationships across models with different $K$. This can... | http://arxiv.org/abs/2109.05541v2 | 2021-09-12T15:49:37Z | stat.AP, stat.CO | 2,021 |
Bayesian Topic Regression for Causal Inference | Maximilian Ahrens, Julian Ashwin, Jan-Peter Calliess, Vu Nguyen | Causal inference using observational text data is becoming increasingly popular in many research areas. This paper presents the Bayesian Topic Regression (BTR) model that uses both text and numerical information to model an outcome variable. It allows estimation of both discrete and continuous treatment effects. Furthe... | http://arxiv.org/abs/2109.05317v1 | 2021-09-11T16:40:43Z | stat.ML, cs.CL, cs.LG | 2,021 |
Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking | Mayank Soni, Benjamin Cowan, Vincent Wade | Neural language modelling has progressed the state-of-the-art in different downstream Natural Language Processing (NLP) tasks. One such area is of open-domain dialog modelling, neural dialog models based on GPT-2 such as DialoGPT have shown promising performance in single-turn conversation. However, such (neural) dialo... | http://arxiv.org/abs/2109.05090v3 | 2021-09-10T20:06:27Z | cs.CL | 2,021 |
Narratives in economics | Michael Roos, Matthias Reccius | There is growing awareness within the economics profession of the important role narratives play in the economy. Even though empirical approaches that try to quantify economic narratives are getting increasingly popular, there is no theory or even a universally accepted definition of economic narratives underlying this... | http://arxiv.org/abs/2109.02331v2 | 2021-09-06T10:05:08Z | econ.GN, q-fin.EC | 2,021 |
Recommending Researchers in Machine Learning based on Author-Topic Model | Deepak Sharma, Bijendra Kumar, Satish Chand | The aim of this paper is to uncover the researchers in machine learning using the author-topic model (ATM). We collect 16,855 scientific papers from six top journals in the field of machine learning published from 1997 to 2016 and analyze them using ATM. The dataset is broken down into 4 intervals to identify the top r... | http://arxiv.org/abs/2109.02022v1 | 2021-09-05T08:16:10Z | cs.IR, H.4, I.7 | 2,021 |
Effective user intent mining with unsupervised word representation models and topic modelling | Bencheng Wei | Understanding the intent behind chat between customers and customer service agents has become a crucial problem nowadays due to an exponential increase in the use of the Internet by people from different cultures and educational backgrounds. More importantly, the explosion of e-commerce has led to a significant increas... | http://arxiv.org/abs/2109.01765v1 | 2021-09-04T01:52:12Z | cs.AI | 2,021 |
Dynamic Games in Empirical Industrial Organization | Victor Aguirregabiria, Allan Collard-Wexler, Stephen P. Ryan | This survey is organized around three main topics: models, econometrics, and empirical applications. Section 2 presents the theoretical framework, introduces the concept of Markov Perfect Nash Equilibrium, discusses existence and multiplicity, and describes the representation of this equilibrium in terms of conditional... | http://arxiv.org/abs/2109.01725v2 | 2021-09-03T20:45:43Z | econ.EM | 2,021 |
Chronic Pain and Language: A Topic Modelling Approach to Personal Pain Descriptions | Diogo A. P. Nunes, Joana Ferreira Gomes, Fani Neto, David Martins de Matos | Chronic pain is recognized as a major health problem, with impacts not only at the economic, but also at the social, and individual levels. Being a private and subjective experience, it is impossible to externally and impartially experience, describe, and interpret chronic pain as a purely noxious stimulus that would d... | http://arxiv.org/abs/2109.00402v2 | 2021-09-01T14:31:16Z | cs.CL, cs.IR, q-bio.QM, I.2.7; I.5.3; I.5.4; J.3; J.4 | 2,021 |
STFT-LDA: An Algorithm to Facilitate the Visual Analysis of Building Seismic Responses | Zhenge Zhao, Danilo Motta, Matthew Berger, Joshua A. Levine, Ismail B. Kuzucu, Robert B. Fleischman, Afonso Paiva, Carlos Scheidegger | Civil engineers use numerical simulations of a building's responses to seismic forces to understand the nature of building failures, the limitations of building codes, and how to determine the latter to prevent the former. Such simulations generate large ensembles of multivariate, multiattribute time series. Comprehens... | http://arxiv.org/abs/2109.00197v1 | 2021-09-01T05:47:05Z | cs.HC | 2,021 |
Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence | Chad A Melton, Olufunto A Olusanya, Nariman Ammar, Arash Shaban-Nejad | The COVID-19 pandemic fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy. To investigate COVID-19 vaccine-related discussion in social media, we conducted a sentiment analysis and Latent Dirichl... | http://arxiv.org/abs/2108.13293v1 | 2021-08-22T00:11:19Z | cs.IR, cs.SI, 68T50, I.2.7; J.3 | 2,021 |
A Framework for Neural Topic Modeling of Text Corpora | Shayan Fazeli, Majid Sarrafzadeh | Topic Modeling refers to the problem of discovering the main topics that have occurred in corpora of textual data, with solutions finding crucial applications in numerous fields. In this work, inspired by the recent advancements in the Natural Language Processing domain, we introduce FAME, an open-source framework enab... | http://arxiv.org/abs/2108.08946v1 | 2021-08-19T23:32:38Z | cs.CL, cs.LG | 2,021 |
MigrationsKB: A Knowledge Base of Public Attitudes towards Migrations and their Driving Factors | Yiyi Chen, Harald Sack, Mehwish Alam | With the increasing trend in the topic of migration in Europe, the public is now more engaged in expressing their opinions through various platforms such as Twitter. Understanding the online discourses is therefore essential to capture the public opinion. The goal of this study is the analysis of social media platform ... | http://arxiv.org/abs/2108.07593v1 | 2021-08-17T12:50:39Z | cs.CL, cs.AI, 68T50, 68T07 | 2,021 |
Generating Cyber Threat Intelligence to Discover Potential Security Threats Using Classification and Topic Modeling | Md Imran Hossen, Ashraful Islam, Farzana Anowar, Eshtiak Ahmed, Mohammad Masudur Rahman, Xiali, Hei | Due to the variety of cyber-attacks or threats, the cybersecurity community enhances the traditional security control mechanisms to an advanced level so that automated tools can encounter potential security threats. Very recently, Cyber Threat Intelligence (CTI) has been presented as one of the proactive and robust mec... | http://arxiv.org/abs/2108.06862v3 | 2021-08-16T02:30:29Z | cs.LG, cs.CR | 2,021 |
A Random Matrix Perspective on Random Tensors | José Henrique de Morais Goulart, Romain Couillet, Pierre Comon | Tensor models play an increasingly prominent role in many fields, notably in machine learning. In several applications, such as community detection, topic modeling and Gaussian mixture learning, one must estimate a low-rank signal from a noisy tensor. Hence, understanding the fundamental limits of estimators of that si... | http://arxiv.org/abs/2108.00774v2 | 2021-08-02T10:42:22Z | stat.ML, cs.LG, math.PR, 15A69, 60B20 | 2,021 |
An Empirical Study of Developers' Discussions about Security Challenges of Different Programming Languages | Roland Croft, Yongzheng Xie, Mansooreh Zahedi, M. Ali Babar, Christoph Treude | Given programming languages can provide different types and levels of security support, it is critically important to consider security aspects while selecting programming languages for developing software systems. Inadequate consideration of security in the choice of a programming language may lead to potential ramifi... | http://arxiv.org/abs/2107.13723v2 | 2021-07-29T03:19:52Z | cs.SE, cs.CR | 2,021 |
Measuring daily-life fear perception change: a computational study in the context of COVID-19 | Yuchen Chai, Juan Palacios, Jianghao Wang, Yichun Fan, Siqi Zheng | COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. Here, we constru... | http://arxiv.org/abs/2107.12606v1 | 2021-07-27T05:17:09Z | cs.CL | 2,021 |
Out of the Shadows: Analyzing Anonymous' Twitter Resurgence during the 2020 Black Lives Matter Protests | Keenan Jones, Jason R. C. Nurse, Shujun Li | Recently, there had been little notable activity from the once prominent hacktivist group, Anonymous. The group, responsible for activist-based cyber attacks on major businesses and governments, appeared to have fragmented after key members were arrested in 2013. In response to the major Black Lives Matter (BLM) protes... | http://arxiv.org/abs/2107.10554v1 | 2021-07-22T10:18:32Z | cs.CY, cs.LG | 2,021 |
Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical Data | Szymon Sacher, Laura Battaglia, Stephen Hansen | Latent variable models are increasingly used in economics for high-dimensional categorical data like text and surveys. We demonstrate the effectiveness of Hamiltonian Monte Carlo (HMC) with parallelized automatic differentiation for analyzing such data in a computationally efficient and methodologically sound manner. O... | http://arxiv.org/abs/2107.08112v2 | 2021-07-16T20:40:54Z | econ.EM, stat.ME | 2,021 |
Modeling User Behaviour in Research Paper Recommendation System | Arpita Chaudhuri, Debasis Samanta, Monalisa Sarma | User intention which often changes dynamically is considered to be an important factor for modeling users in the design of recommendation systems. Recent studies are starting to focus on predicting user intention (what users want) beyond user preference (what users like). In this work, a user intention model is propose... | http://arxiv.org/abs/2107.07831v1 | 2021-07-16T11:31:03Z | cs.IR, cs.LG | 2,021 |
Tales of a City: Sentiment Analysis of Urban Green Space in Dublin | Mohammadhossein Ghahramani, Nadina Galle, Carlo Ratti, Francesco Pilla | Social media services such as TripAdvisor and Foursquare can provide opportunities for users to exchange their opinions about urban green space (UGS). Visitors can exchange their experiences with parks, woods, and wetlands in social communities via social networks. In this work, we implement a unified topic modeling ap... | http://arxiv.org/abs/2107.06041v1 | 2021-07-13T12:51:46Z | cs.SI | 2,021 |
Semiparametric Latent Topic Modeling on Consumer-Generated Corpora | Dominic B. Dayta, Erniel B. Barrios | Legacy procedures for topic modelling have generally suffered problems of overfitting and a weakness towards reconstructing sparse topic structures. With motivation from a consumer-generated corpora, this paper proposes semiparametric topic model, a two-step approach utilizing nonnegative matrix factorization and semip... | http://arxiv.org/abs/2107.10651v1 | 2021-07-13T00:22:02Z | cs.CL, cs.LG | 2,021 |
Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations | Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp | This paper studies the estimation of high-dimensional, discrete, possibly sparse, mixture models in topic models. The data consists of observed multinomial counts of $p$ words across $n$ independent documents. In topic models, the $p\times n$ expected word frequency matrix is assumed to be factorized as a $p\times K$ w... | http://arxiv.org/abs/2107.05766v2 | 2021-07-12T22:22:32Z | math.ST, stat.ME, stat.ML, stat.TH | 2,021 |
Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective | Cynthia Pagliaro, Dhagash Mehta, Han-Tai Shiao, Shaofei Wang, Luwei Xiong | Modeling investor behavior is crucial to identifying behavioral coaching opportunities for financial advisors. With the help of natural language processing (NLP) we analyze an unstructured (textual) dataset of financial advisors' summary notes, taken after every investor conversation, to gain first ever insights into a... | http://arxiv.org/abs/2107.05592v1 | 2021-07-12T17:12:30Z | q-fin.ST, q-fin.CP, stat.AP | 2,021 |
Assigning Topics to Documents by Successive Projections | Olga Klopp, Maxim Panov, Suzanne Sigalla, Alexandre Tsybakov | Topic models provide a useful tool to organize and understand the structure of large corpora of text documents, in particular, to discover hidden thematic structure. Clustering documents from big unstructured corpora into topics is an important task in various areas, such as image analysis, e-commerce, social networks,... | http://arxiv.org/abs/2107.03684v1 | 2021-07-08T08:58:35Z | math.ST, stat.TH | 2,021 |
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics | Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick | Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks. Prior specification is, however, relatively difficult for such models, given that their flexibility implies that the consequences o... | http://arxiv.org/abs/2107.03584v3 | 2021-07-08T03:40:18Z | stat.ME, stat.CO, stat.ML | 2,021 |
Topic Modeling in the Voynich Manuscript | Rachel Sterneck, Annie Polish, Claire Bowern | This article presents the results of investigations using topic modeling of the Voynich Manuscript (Beinecke MS408). Topic modeling is a set of computational methods which are used to identify clusters of subjects within text. We use latent dirichlet allocation, latent semantic analysis, and nonnegative matrix factoriz... | http://arxiv.org/abs/2107.02858v1 | 2021-07-06T19:50:03Z | cs.CL | 2,021 |
Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence | Alexander Hoyle, Pranav Goel, Denis Peskov, Andrew Hian-Cheong, Jordan Boyd-Graber, Philip Resnik | Topic model evaluation, like evaluation of other unsupervised methods, can be contentious. However, the field has coalesced around automated estimates of topic coherence, which rely on the frequency of word co-occurrences in a reference corpus. Contemporary neural topic models surpass classical ones according to these ... | http://arxiv.org/abs/2107.02173v3 | 2021-07-05T17:58:52Z | cs.CL, cs.LG | 2,021 |
Evaluation of Thematic Coherence in Microblogs | Iman Munire Bilal, Bo Wang, Maria Liakata, Rob Procter, Adam Tsakalidis | Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners. A major question is how to evaluate the quality of such thematic clusters. Here we create a corpus of microblog clusters from three different domains and time ... | http://arxiv.org/abs/2106.15971v1 | 2021-06-30T10:32:59Z | cs.CL | 2,021 |
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network | Zhibin Duan, Dongsheng Wang, Bo Chen, Chaojie Wang, Wenchao Chen, Yewen Li, Jie Ren, Mingyuan Zhou | Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic taxonomies. However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ... | http://arxiv.org/abs/2107.02757v1 | 2021-06-30T10:14:57Z | cs.IR, cs.CL, cs.LG | 2,021 |
The rise of populism and the reconfiguration of the German political space | Eckehard Olbrich, Sven Banisch | The paper explores the notion of a reconfiguration of political space in the context of the rise of populism and its effects on the political system. We focus on Germany and the appearance of the new right wing party "Alternative for Germany" (AfD). Many scholars of politics discuss the rise of the new populism in West... | http://arxiv.org/abs/2106.15717v2 | 2021-06-29T20:43:45Z | physics.soc-ph, cs.SI | 2,021 |
Topic Modeling Based Extractive Text Summarization | Kalliath Abdul Rasheed Issam, Shivam Patel, Subalalitha C. N | Text summarization is an approach for identifying important information present within text documents. This computational technique aims to generate shorter versions of the source text, by including only the relevant and salient information present within the source text. In this paper, we propose a novel method to sum... | http://arxiv.org/abs/2106.15313v1 | 2021-06-29T12:28:19Z | cs.CL, cs.IR | 2,021 |
Integrating topic modeling and word embedding to characterize violent deaths | Alina Arseniev-Koehler, Susan D. Cochran, Vickie M. Mays, Kai-Wei Chang, Jacob Gates Foster | There is an escalating need for methods to identify latent patterns in text data from many domains. We introduce a new method to identify topics in a corpus and represent documents as topic sequences. Discourse Atom Topic Modeling draws on advances in theoretical machine learning to integrate topic modeling and word em... | http://arxiv.org/abs/2106.14365v1 | 2021-06-28T01:53:20Z | cs.CL, cs.CY, cs.LG | 2,021 |
Recurrent Coupled Topic Modeling over Sequential Documents | Jinjin Guo, Longbing Cao, Zhiguo Gong | The abundant sequential documents such as online archival, social media and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and th... | http://arxiv.org/abs/2106.13732v1 | 2021-06-23T08:58:13Z | cs.IR, cs.LG | 2,021 |
Towards a corpus for credibility assessment in software practitioner blog articles | Ashley Williams, Matthew Shardlow, Austen Rainer | Blogs are a source of grey literature which are widely adopted by software practitioners for disseminating opinion and experience. Analysing such articles can provide useful insights into the state-of-practice for software engineering research. However, there are challenges in identifying higher quality content from th... | http://arxiv.org/abs/2106.11159v1 | 2021-06-21T14:57:13Z | cs.SE | 2,021 |
Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection | Yixiao Wang, Zied Bouraoui, Luis Espinosa Anke, Steven Schockaert | One of the long-standing challenges in lexical semantics consists in learning representations of words which reflect their semantic properties. The remarkable success of word embeddings for this purpose suggests that high-quality representations can be obtained by summarizing the sentence contexts of word mentions. In ... | http://arxiv.org/abs/2106.07947v1 | 2021-06-15T08:02:42Z | cs.CL | 2,021 |
Insight from NLP Analysis: COVID-19 Vaccines Sentiments on Social Media | Tao Na, Wei Cheng, Dongming Li, Wanyu Lu, Hongjiang Li | Social media is an appropriate source for analyzing public attitudes towards the COVID-19 vaccine and various brands. Nevertheless, there are few relevant studies. In the research, we collected tweet posts by the UK and US residents from the Twitter API during the pandemic and designed experiments to answer three main ... | http://arxiv.org/abs/2106.04081v1 | 2021-06-08T03:37:22Z | cs.CL, cs.SI | 2,021 |
Surveillance of COVID-19 Pandemic using Social Media: A Reddit Study in North Carolina | Christopher Whitfield, Yang Liu, Mohd Anwar | Coronavirus disease (COVID-19) pandemic has changed various aspects of people's lives and behaviors. At this stage, there are no other ways to control the natural progression of the disease than adopting mitigation strategies such as wearing masks, watching distance, and washing hands. Moreover, at this time of social ... | http://arxiv.org/abs/2106.04515v3 | 2021-06-07T06:55:25Z | cs.SI, cs.IR, cs.LG | 2,021 |
Network-based Topic Interaction Map for Big Data Mining of COVID-19 Biomedical Literature | Yeseul Jeon, Dongjun Chung, Jina Park, Ick Hoon Jin | Since the emergence of the worldwide pandemic of COVID-19, relevant research has been published at a dazzling pace, which yields an abundant amount of big data in biomedical literature. Due to the high volum of relevant literature, it is practically impossible to follow up the research manually. Topic modeling is a wel... | http://arxiv.org/abs/2106.07374v4 | 2021-06-07T06:01:17Z | cs.IR, stat.AP | 2,021 |
A protocol to gather, characterize and analyze incoming citations of retracted articles | Ivan Heibi, Silvio Peroni | In this article, we present a methodology which takes as input a collection of retracted articles, gathers the entities citing them, characterizes such entities according to multiple dimensions (disciplines, year of publication, sentiment, etc.), and applies a quantitative and qualitative analysis on the collected valu... | http://arxiv.org/abs/2106.01781v1 | 2021-06-03T12:09:41Z | cs.DL | 2,021 |
T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT | Sarojadevi Palani, Prabhu Rajagopal, Sidharth Pancholi | Sentiment analysis (SA) has become an extensive research area in recent years impacting diverse fields including ecommerce, consumer business, and politics, driven by increasing adoption and usage of social media platforms. It is challenging to extract topics and sentiments from unsupervised short texts emerging in suc... | http://arxiv.org/abs/2106.01097v1 | 2021-06-02T12:01:47Z | cs.CL, cs.AI | 2,021 |
A Query-Driven Topic Model | Zheng Fang, Yulan He, Rob Procter | Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. It has been increasingly widely adopted as a tool in the social sciences, including political science, digital humanities and sociological research in general. One desirable property of topic models is to allow users to fi... | http://arxiv.org/abs/2106.07346v2 | 2021-05-28T22:49:42Z | cs.IR, cs.LG | 2,021 |
Non-negative matrix factorization algorithms greatly improve topic model fits | Peter Carbonetto, Abhishek Sarkar, Zihao Wang, Matthew Stephens | We report on the potential for using algorithms for non-negative matrix factorization (NMF) to improve parameter estimation in topic models. While several papers have studied connections between NMF and topic models, none have suggested leveraging these connections to develop new algorithms for fitting topic models. NM... | http://arxiv.org/abs/2105.13440v2 | 2021-05-27T20:34:46Z | stat.ML, cs.LG, stat.CO | 2,021 |
On the Globalization of the QAnon Conspiracy Theory Through Telegram | Mohamad Hoseini, Philipe Melo, Fabricio Benevenuto, Anja Feldmann, Savvas Zannettou | QAnon is a far-right conspiracy theory that became popular and mainstream over the past few years. Worryingly, the QAnon conspiracy theory has implications in the real world, with supporters of the theory participating in real-world violent acts like the US capitol attack in 2021. At the same time, the QAnon theory sta... | http://arxiv.org/abs/2105.13020v1 | 2021-05-27T09:24:25Z | cs.CY, cs.SI | 2,021 |
Topic Modeling and Progression of American Digital News Media During the Onset of the COVID-19 Pandemic | Xiangpeng Wan, Michael C. Lucic, Hakim Ghazzai, Yehia Massoud | Currently, the world is in the midst of a severe global pandemic, which has affected all aspects of people's lives. As a result, there is a deluge of COVID-related digital media articles published in the United States, due to the disparate effects of the pandemic. This large volume of information is difficult to consum... | http://arxiv.org/abs/2106.09572v1 | 2021-05-25T14:27:47Z | cs.CL | 2,021 |
Have you tried Neural Topic Models? Comparative Analysis of Neural and Non-Neural Topic Models with Application to COVID-19 Twitter Data | Andrew Bennett, Dipendra Misra, Nga Than | Topic models are widely used in studying social phenomena. We conduct a comparative study examining state-of-the-art neural versus non-neural topic models, performing a rigorous quantitative and qualitative assessment on a dataset of tweets about the COVID-19 pandemic. Our results show that not only do neural topic mod... | http://arxiv.org/abs/2105.10165v1 | 2021-05-21T07:24:09Z | cs.CL, cs.CY, cs.IR, cs.LG | 2,021 |
Variational Gaussian Topic Model with Invertible Neural Projections | Rui Wang, Deyu Zhou, Yuxuan Xiong, Haiping Huang | Neural topic models have triggered a surge of interest in extracting topics from text automatically since they avoid the sophisticated derivations in conventional topic models. However, scarce neural topic models incorporate the word relatedness information captured in word embedding into the modeling process. To addre... | http://arxiv.org/abs/2105.10095v1 | 2021-05-21T02:23:02Z | cs.AI | 2,021 |
Learning a Latent Simplex in Input-Sparsity Time | Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou | We consider the problem of learning a latent $k$-vertex simplex $K\subset\mathbb{R}^d$, given access to $A\in\mathbb{R}^{d\times n}$, which can be viewed as a data matrix with $n$ points that are obtained by randomly perturbing latent points in the simplex $K$ (potentially beyond $K$). A large class of latent variable ... | http://arxiv.org/abs/2105.08005v1 | 2021-05-17T16:40:48Z | cs.LG, cs.DS, stat.ML | 2,021 |
Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning | Dandan Guo, Ruiying Lu, Bo Chen, Zequn Zeng, Mingyuan Zhou | Observing a set of images and their corresponding paragraph-captions, a challenging task is to learn how to produce a semantically coherent paragraph to describe the visual content of an image. Inspired by recent successes in integrating semantic topics into this task, this paper develops a plug-and-play hierarchical-t... | http://arxiv.org/abs/2105.04143v2 | 2021-05-10T06:55:39Z | cs.CV, stat.ML | 2,021 |
GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcripts | Lukas Stappen, Jason Thies, Gerhard Hagerer, Björn W. Schuller, Georg Groh | To unfold the tremendous amount of multimedia data uploaded daily to social media platforms, effective topic modeling techniques are needed. Existing work tends to apply topic models on written text datasets. In this paper, we propose a topic extractor on video transcripts. Exploiting neural word embeddings through gra... | http://arxiv.org/abs/2105.01466v4 | 2021-05-04T12:48:17Z | cs.CL, cs.MM | 2,021 |
Supervised multi-specialist topic model with applications on large-scale electronic health record data | Ziyang Song, Xavier Sumba Toral, Yixin Xu, Aihua Liu, Liming Guo, Guido Powell, Aman Verma, David Buckeridge, Ariane Marelli, Yue Li | Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. We present MixEHR-S to jointly infer specialist-disease topics f... | http://arxiv.org/abs/2105.01238v1 | 2021-05-04T01:27:11Z | cs.LG, q-bio.QM | 2,021 |
Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset | W. Y. Ayele | Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate ... | http://arxiv.org/abs/2105.00574v1 | 2021-05-02T23:24:25Z | cs.IR, cs.CL, cs.LG | 2,021 |
Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems | Ziming Li, Julia Kiseleva, Maarten de Rijke | Being able to generate informative and coherent dialogue responses is crucial when designing human-like open-domain dialogue systems. Encoder-decoder-based dialogue models tend to produce generic and dull responses during the decoding step because the most predictable response is likely to be a non-informative response... | http://arxiv.org/abs/2105.00079v1 | 2021-04-30T20:38:27Z | cs.CL | 2,021 |
Analysis of Legal Documents via Non-negative Matrix Factorization Methods | Ryan Budahazy, Lu Cheng, Yihuan Huang, Andrew Johnson, Pengyu Li, Joshua Vendrow, Zhoutong Wu, Denali Molitor, Elizaveta Rebrova, Deanna Needell | The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files. Processing and interpreting this large amount of information presents a significant challenge for CIP off... | http://arxiv.org/abs/2104.14028v2 | 2021-04-28T21:32:22Z | cs.LG, cs.CY | 2,021 |
A Comprehensive Attempt to Research Statement Generation | Wenhao Wu, Sujian Li | For a researcher, writing a good research statement is crucial but costs a lot of time and effort. To help researchers, in this paper, we propose the research statement generation (RSG) task which aims to summarize one's research achievements and help prepare a formal research statement. For this task, we conduct a com... | http://arxiv.org/abs/2104.14339v1 | 2021-04-25T03:57:00Z | cs.IR, cs.CL | 2,021 |
Deep Probabilistic Graphical Modeling | Adji B. Dieng | Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years. DL... | http://arxiv.org/abs/2104.12053v1 | 2021-04-25T03:48:02Z | stat.ML, cs.LG | 2,021 |
Clustering Introductory Computer Science Exercises Using Topic Modeling Methods | Laura O. Moraes, Carlos Eduardo Pedreira | Manually determining concepts present in a group of questions is a challenging and time-consuming process. However, the process is an essential step while modeling a virtual learning environment since a mapping between concepts and questions using mastery level assessment and recommendation engines are required. We inv... | http://arxiv.org/abs/2104.10748v1 | 2021-04-21T20:23:53Z | cs.LG, cs.CL, cs.IR | 2,021 |
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