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Found 27 papers, 9 papers with code
A Deep Learning Approach for Selective Relevance Feedback
Additionally, to further improve retrieval effectiveness with this selective PRF approach, we make use of the model's confidence estimates to combine the information from the original and expanded queries.
RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models
In the evolving field of personalized news recommendation, understanding the semantics of the underlying data is crucial.
The Role of Document Embedding in Research Paper Recommender Systems: To Breakdown or to Bolster Disciplinary Borders?
In the extensive recommender systems literature, novelty and diversity have been identified as key properties of useful recommendations.
Topic-Centric Explanations for News Recommendation
News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests.
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts
The increasing availability of digital collections of historical and contemporary literature presents a wealth of possibilities for new research in the humanities.
On the Feasibility and Robustness of Pointwise Evaluation of Query Performance Prediction
Despite the retrieval effectiveness of queries being mutually independent of one another, the evaluation of query performance prediction (QPP) systems has been carried out by measuring rank correlation over an entire set of queries.
Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ
Counterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems due to their psychological validity, flexibility across problem domains and proposed legal compliance.
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification
Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline.
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction
In contrast to unsupervised approaches that rely on various statistics of document score distributions, our approach is entirely data-driven.
An Analysis of Variations in the Effectiveness of Query Performance Prediction
An important characteristic of QPP evaluation is that, since the ground truth retrieval effectiveness for QPP evaluation can be measured with different metrics, the ground truth itself is not absolute, which is in contrast to other retrieval tasks, such as that of ad-hoc retrieval.
Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions
Whilst an abundance of techniques have recently been proposed to generate counterfactual explanations for the predictions of opaque black-box systems, markedly less attention has been paid to exploring the uncertainty of these generated explanations.
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation
Recently, it has been proposed that fruitful synergies may exist between Deep Learning (DL) and Case Based Reasoning (CBR); that there are insights to be gained by applying CBR ideas to problems in DL (what could be called DeepCBR).
Instance-based Counterfactual Explanations for Time Series Classification
In recent years, there has been a rapidly expanding focus on explaining the predictions made by black-box AI systems that handle image and tabular data.
Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography
Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging.
Mitigating Gender Bias in Machine Learning Data Sets
Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society.
Temporal Analysis of Reddit Networks via Role Embeddings
Inspired by diachronic word analysis from the field of natural language processing, we propose an approach for uncovering temporal insights regarding user roles from social networks using graph embedding methods.
MeetupNet Dublin: Discovering Communities in Dublin's Meetup Network
Meetup. com is a global online platform which facilitates the organisation of meetups in different parts of the world.
Social and Information Networks Computers and Society
Stability of Topic Modeling via Matrix Factorization
Topic models can provide us with an insight into the underlying latent structure of a large corpus of documents.
EVE: Explainable Vector Based Embedding Technique Using Wikipedia
We present an unsupervised explainable word embedding technique, called EVE, which is built upon the structure of Wikipedia.
Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach
To unveil the plenary agenda and detect latent themes in legislative speeches over time, MEP speech content is analyzed using a new dynamic topic modeling method based on two layers of Non-negative Matrix Factorization (NMF).
Indicators of Good Student Performance in Moodle Activity Data
In this paper we conduct an analysis of Moodle activity data focused on identifying early predictors of good student performance.
Topic Stability over Noisy Sources
Topic modelling techniques such as LDA have recently been applied to speech transcripts and OCR output.
Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis
This study analyzes political interactions in the European Parliament (EP) by considering how the political agenda of the plenary sessions has evolved over time and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making Parliamentary speeches.
A Latent Space Analysis of Editor Lifecycles in Wikipedia
Collaborations such as Wikipedia are a key part of the value of the modern Internet.
How Many Topics? Stability Analysis for Topic Models
Topic modeling refers to the task of discovering the underlying thematic structure in a text corpus, where the output is commonly presented as a report of the top terms appearing in each topic.
Adaptive Representations for Tracking Breaking News on Twitter
In this paper we examine the effectiveness of adaptive mechanisms for tracking and summarizing breaking news stories.
Normalized Mutual Information to evaluate overlapping community finding algorithms
Given the increasing popularity of algorithms for overlapping clustering, in particular in social network analysis, quantitative measures are needed to measure the accuracy of a method.
Physics and Society Social and Information Networks Data Analysis, Statistics and Probability
