Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin

Numerical Complete Solution for Random Genetic Drift by Energetic Variational Approach

In this paper, we focus on numerical solutions for random genetic drift problem, which is governed by a degenerated convection-dominated parabolic equation. Due to the fixation phenomenon of genes, Dirac delta singularities will develop at boundary points as time evolves. Based on an energetic variational approach (EnVarA), a balance between the maximal dissipation principle (MDP) and least action principle (LAP), we obtain the trajectory equation. In turn, a numerical scheme is proposed using a convex splitting technique, with the unique solvability (on a convex set) and the energy decay property (in time) justified at a theoretical level. Numerical examples are presented for cases of pure drift and drift with semi-selection. The remarkable advantage of this method is its ability to catch the Dirac delta singularity close to machine precision over any equidistant grid.Comment: 22 pages, 11 figures, 2 table

Sherlock : a Semi-Automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure

Acknowledgments This work is supported by the BBC Connected Studio programme (http://www.bbc.co.uk/partnersandsuppliers/con nectedstudio/), the award made by the RCUK Digital Economy theme to the dot.rural Digital Economy Hub; award reference EP/G066051/1, the award made by UK Economic & Social Research Council (ESRC); award reference ES/M001628/1, National Natural Science Foundation of China (NSFC) under Grant No. 61373051, and the China National Science and Technology Pillar Program (Grant No. 2013BAH07F05). The authors would like to thank Ryan Hussey for the work on the user interface design and Tom Cass and James Ruston for the help in developing the Sherlock application. We are also grateful to Herm Baskerville for creating the editorial quizzes and Nava Tintarev for many helpful discussions on the human evaluation.Peer reviewedPublisher PD

Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers

Abstracts derived from biomedical literature possess distinct domain-specific characteristics, including specialised writing styles and biomedical terminologies, which necessitate a deep understanding of the related literature. As a result, existing language models struggle to generate technical summaries that are on par with those produced by biomedical experts, given the absence of domain-specific background knowledge. This paper aims to enhance the performance of language models in biomedical abstractive summarisation by aggregating knowledge from external papers cited within the source article. We propose a novel attention-based citation aggregation model that integrates domain-specific knowledge from citation papers, allowing neural networks to generate summaries by leveraging both the paper content and relevant knowledge from citation papers. Furthermore, we construct and release a large-scale biomedical summarisation dataset that serves as a foundation for our research. Extensive experiments demonstrate that our model outperforms state-of-the-art approaches and achieves substantial improvements in abstractive biomedical text summarisation.Comment: Accepted by EMNLP 202

Metaphor Detection via Explicit Basic Meanings Modelling

One noticeable trend in metaphor detection is the embrace of linguistic theories such as the metaphor identification procedure (MIP) for model architecture design. While MIP clearly defines that the metaphoricity of a lexical unit is determined based on the contrast between its \textit{contextual meaning} and its \textit{basic meaning}, existing work does not strictly follow this principle, typically using the \textit{aggregated meaning} to approximate the basic meaning of target words. In this paper, we propose a novel metaphor detection method, which models the basic meaning of the word based on literal annotation from the training set, and then compares this with the contextual meaning in a target sentence to identify metaphors. Empirical results show that our method outperforms the state-of-the-art method significantly by 1.0\% in F1 score. Moreover, our performance even reaches the theoretical upper bound on the VUA18 benchmark for targets with basic annotations, which demonstrates the importance of modelling basic meanings for metaphor detection.Comment: ACL 202