• A deep network model for paraphrase detection in short text messages 

      Agarwal, Basant; Ramampiaro, Heri; Langseth, Helge; Ruocco, Massimiliano (Journal article; Peer reviewed, 2018)
      This paper is concerned with paraphrase detection, i.e., identifying sentences that are semantically identical. The ability to detect similar sentences written in natural language is crucial for several applications, such ...
    • Applying temporal dependence to detect changes in streaming data 

      Duong, Quang-Huy; Ramampiaro, Heri; Nørvåg, Kjetil (Journal article; Peer reviewed, 2018)
      Detection of changes in streaming data is an important mining task, with a wide range of real-life ap- plications. Numerous algorithms have been proposed to efficiently detect changes in streaming data. However, the ...
    • Bayesian Text Categorization 

      Næss, Arild Brandrud (Master thesis, 2007)
      Natural language processing is an interdisciplinary field of research which studies the problems and possibilities of automated generation and understanding of natural human languages. Text categorization is a central ...
    • Effective hate-speech detection in Twitter data using recurrent neural networks 

      Pitsilis, Georgios; Ramampiaro, Heri; Langseth, Helge (Journal article; Peer reviewed, 2018)
      This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features ...
    • Event Detection in Changing and Evolving Environments 

      Duong, Huy Quang (Doctoral theses at NTNU;2021:61, Doctoral thesis, 2021)
      The availability of modern technology and the recent proliferation of devices and sensors have resulted in a tremendous amount of data being generated, stored and handled in various applications that affect almost all ...
    • Explaining deep learning methods for recommender systems 

      Bjørgen, Erik; Hambro, Carl Johan (Master thesis, 2019)
      Systemkrav for anbefalingssystemer er i stadig endring. Formatet til anbefalinger, samt GDPR, har bragt med seg nye krav for anbefalingene som blir gitt. Forklarbarhet eller tolkbarhet er ett av disse nye kravene. orklarbarhet ...
    • Explaining deep learning methods for recommender systems 

      Bjørgen, Erik; Hambro, Carl Johan (Master thesis, 2019)
      Systemkrav for anbefalingssystemer er i stadig endring. Formatet til anbefalinger, samt GDPR, har bragt med seg nye krav for anbefalingene som blir gitt. Forklarbarhet eller tolkbarhet er ett av disse nye kravene. Forklarbarhet ...
    • Exploring Decomposition for Solving Pattern Mining Problems 

      Djenouri, Youcef; Lin, Jerry Chun-Wei; Nørvåg, Kjetil; Ramampiaro, Heri; Yu, Philip S. (Peer reviewed; Journal article, 2021)
      This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction ...
    • Extracting news events from microblogs 

      Øystein, Repp; Ramampiaro, Heri (Journal article; Peer reviewed, 2018)
      Twitter stream has become a large source of information, but the magnitude of tweets posted and the noisy nature of its content makes harvesting of knowledge from Twitter has challenged researchers for long time. Aiming ...
    • Fast and accurate group outlier detection for trajectory data 

      Djenouri, Youcef; Nørvåg, Kjetil; Ramampiaro, Heri; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2020)
      Previous approaches to solve the trajectory outlier detection problem exclusively examine single outliers. However, anomalies in trajectory data may often occur in groups. This paper introduces a new problem, group trajectory ...
    • Forecasting Intra-Hour Imbalances in Electric Power Systems 

      Saleh Salem, Tárik; Kathuria, Karan; Ramampiaro, Heri; Langseth, Helge (Journal article; Peer reviewed, 2019)
      Keeping the electricity production in balance with the actual demand is becoming a difficult and expensive task in spite of an involvement of experienced human operators. This is due to the increasing complexity of the ...
    • High Utility Drift Detection in Quantitative Data Streams 

      Duong, Quang-Huy; Ramampiaro, Heri; Nørvåg, Kjetil; Fournier-Viger, Philippe; Dam, Thu-Lan (Journal article; Peer reviewed, 2018)
      This paper presents an efficient algorithm for detecting changes (drifts) in the utility distributions of patterns, named High Utility Drift Detection in Transactional Data Stream (HUDD-TDS). The algorithm is specifically ...
    • Highly Efficient Pattern Mining Based on Transaction Decomposition 

      Djenouri, Youcef; Lin, Jerry Chun-Wei; Nørvåg, Kjetil; Ramampiaro, Heri (Chapter; Peer reviewed, 2019)
      This paper introduces a highly efficient pattern mining technique called Clustering-Based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in transaction ...
    • Locality-adapted kernel densities of term co-occurrences for location prediction of tweets 

      Özdikis, Özer; Ramampiaro, Heri; Nørvåg, Kjetil (Journal article; Peer reviewed, 2019)
      While geographical metadata referring to the originating locations of tweets provides valuable information to perform effective spatial analysis in social networks, scarcity of such geotagged tweets imposes limitations on ...
    • Location prediction using neural networks 

      Løkken, Erlend; Waatevik, Endre (Master thesis, 2019)
      For å kunne studere brukeres bevegelser og handlinger er det viktig å identifisere lokasjonen en tweet er skrevet fra eller omhandler. Denne typen informasjon kan brukes i en rekke applikasjoner som avhenger av geografisk ...
    • Multiple Dense Subtensor Estimation with High Density Guarantee 

      Duong, Quang-Huy; Ramampiaro, Heri; Nørvåg, Kjetil (Chapter, 2020)
      Dense subtensor detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Existing algorithms are generally efficient for dense subtensor ...
    • New Insights into Content Production in Social Platforms 

      Kusmierczyk, Tomasz (Doctoral theses at NTNU;2018:173, Doctoral thesis, 2018)
      Social media influences to a great extent everyday life of millions of people, and therefore the understanding of certain patterns relating to online platforms and communities, and the ability to model and predict them, ...
    • Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles 

      Saleh Salem, Tárik; Langseth, Helge; Ramampiaro, Heri (Peer reviewed; Journal article, 2020)
      Prediction intervals are a machine- and human-interpretable way to represent predictive uncertainty in a regression analysis. In this paper, we present a method for generating prediction intervals along with point estimates ...
    • Ranking Streaming Data With Continuous Queries 

      Skarshaug, Sandra Marie (Master thesis, 2019)
      En økende mengde data genereres som en del av digitaliseringen av samfunnet vårt. Forskning fra 2013 viser at 90% av all dataen generert i verden frem til det tidspunktet, ble generert i løpet av de to foregående årene. ...
    • Semantic Matching: Dynamic Composition of Matcher Ensembles for Ontology Alignment 

      Vennesland, Audun (Doctoral theses at NTNU;2020:247, Doctoral thesis, 2020)
      Semantic matching is a computational process that aims to automatically identify the semantic relationship between elements represented in different graph-like sources. Typically, this is a process that involves human ...