106 citations · 393 across the 52 of their papers we have counts for
57 papers
Joint Extraction and Classification of Danish Competences for Job Matching
Qiuchi Li, Christina Lioma
The matching of competences, such as skills, occupations or knowledges, is a key desiderata for candidates to be fit for jobs. Automatic extraction of competences from CVs and Jobs…
Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain
Simone Borg Bruun, Christina Lioma, Maria Maistro
Providing personalized recommendations for insurance products is particularly challenging due to the intrinsic and distinctive features of the insurance domain. First, unlike more…
Digital-analog hybrid matrix multiplication processor for optical neural networks
Xiansong Meng, Deming Kong, Kwangwoong Kim +5
The computational demands of modern AI have spurred interest in optical neural networks (ONNs) which offer the potential benefits of increased speed and lower power consumption. Ho…
Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +1
Fairness is an emerging and challenging topic in recommender systems. In recent years, various ways of evaluating and therefore improving fairness have emerged. In this study, we e…
Faithfulness Tests for Natural Language Explanations
Pepa Atanasova, Oana-Maria Camburu, Christina Lioma +3
Explanations of neural models aim to reveal a model's decision-making process for its predictions. However, recent work shows that current methods giving explanations such as salie…
Adapting Pre-trained Language Models for Quantum Natural Language Processing
Qiuchi Li, Benyou Wang, Yudong Zhu +2
The emerging classical-quantum transfer learning paradigm has brought a decent performance to quantum computational models in many tasks, such as computer vision, by enabling a com…
Graph-based Recommendation for Sparse and Heterogeneous User Interactions
Simone Borg Bruun, Kacper Kenji Lesniak, Mirko Biasini +4
Recommender system research has oftentimes focused on approaches that operate on large-scale datasets containing millions of user interactions. However, many small businesses strug…
Template-based Recruitment Email Generation For Job Recommendation
Qiuchi Li, Christina Lioma
Text generation has long been a popular research topic in NLP. However, the task of generating recruitment emails from recruiters to candidates in the job recommendation scenario h…
Principled Multi-Aspect Evaluation Measures of Rankings
Maria Maistro, Lucas Chaves Lima, Jakob Grue Simonsen +1
Information Retrieval evaluation has traditionally focused on defining principled ways of assessing the relevance of a ranked list of documents with respect to a query. Several met…
Learning Recommendations from User Actions in the Item-poor Insurance Domain
Simone Borg Bruun, Maria Maistro, Christina Lioma
While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations i…
Fact Checking with Insufficient Evidence
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma +1
Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions…
Diagnostics-Guided Explanation Generation
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma +1
Explanations shed light on a machine learning model's rationales and can aid in identifying deficiencies in its reasoning process. Explanation generation models are typically train…
Unsupervised Multi-Index Semantic Hashing
Christian Hansen, Casper Hansen, Jakob Grue Simonsen +2
Semantic hashing represents documents as compact binary vectors (hash codes) and allows both efficient and effective similarity search in large-scale information retrieval. The sta…
Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering
Christian Hansen, Casper Hansen, Jakob Grue Simonsen +1
When reasoning about tasks that involve large amounts of data, a common approach is to represent data items as objects in the Hamming space where operations can be done efficiently…
Quantum-inspired Multimodal Fusion for Video Sentiment Analysis
Qiuchi Li, Dimitris Gkoumas, Christina Lioma +1
We tackle the crucial challenge of fusing different modalities of features for multimodal sentiment analysis. Mainly based on neural networks, existing approaches largely model mul…
Multi-Head Self-Attention with Role-Guided Masks
Dongsheng Wang, Casper Hansen, Lucas Chaves Lima +4
The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms. Simply put, the attention mechanisms learn to a…
Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19
Lucas Chaves Lima, Casper Hansen, Christian Hansen +5
This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the…
A Diagnostic Study of Explainability Techniques for Text Classification
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma +1
Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural complexity. Efforts to make the rationales beh…
Unsupervised Semantic Hashing with Pairwise Reconstruction
Casper Hansen, Christian Hansen, Jakob Grue Simonsen +2
Semantic Hashing is a popular family of methods for efficient similarity search in large-scale datasets. In Semantic Hashing, documents are encoded as short binary vectors (i.e., h…
Factuality Checking in News Headlines with Eye Tracking
Christian Hansen, Casper Hansen, Jakob Grue Simonsen +3
We study whether it is possible to infer if a news headline is true or false using only the movement of the human eyes when reading news headlines. Our study with 55 participants w…
Content-aware Neural Hashing for Cold-start Recommendation
Casper Hansen, Christian Hansen, Jakob Grue Simonsen +2
Content-aware recommendation approaches are essential for providing meaningful recommendations for \textit{new} (i.e., \textit{cold-start}) items in a recommender system. We presen…
Generating Fact Checking Explanations
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma +1
Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recen…
Encoding word order in complex embeddings
Benyou Wang, Donghao Zhao, Christina Lioma +3
Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that posi…
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
Isabelle Augenstein, Christina Lioma, Dongsheng Wang +4
We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking we…