Improving Retrieval-Based Question Answering with Deep Inference Models

Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer. In this paper, we present a two-step method that combines information retrieval techniques optimized for question answering with deep learning models for natural language inference in order to tackle the multi-choice question answering in the science domain. For each question-answer pair, we use standard retrieval-based models to find relevant candidate contexts and decompose the main problem into two different sub-problems. First, assign correctness scores for each candidate answer based on the context using retrieval models from Lucene. Second, we use deep learning architectures to compute if a candidate answer can be inferred from some well-chosen context consisting of sentences retrieved from the knowledge base. In the end, all these solvers are combined using a simple neural network to predict the correct answer. This proposed two-step model outperforms the best retrieval-based solver by over 3% in absolute accuracy.Comment: 8 pages, 2 figures, 8 tables, accepted at IJCNN 201

Applicability of the technology acceptance model for widget-based personal learning environments

This contribution presents results from two exploratory studies on technology acceptance and use of widget-based personal learning environments. Methodologically, the investigation carried out applies the unified theory of acceptance and use of technology (UTAUT). With the help of this instrument, the study assesses expert judgments about intentions to use and actual use of the emerging technology of flexibly arranged combinations of use-case-sized mini learning tools. This study aims to explore the applicability of the UTAUT model and questionnaire for widget-based personal learning environments and reports back on the experiences gained with the two studies

An investigation into the use of synthetic zeolites for in situ land reclamation.

This thesis describes an experimental study to determine the feasibility of using zeolite addition for the in situ treatment of soils contaminated with heavy metals. The aim of the present work was to examine the effectiveness of three synthetic zeolites to reduce plant available metal pools in contaminated soils. Three contaminated soils were studied, which are representative of typical contamination sites in the UK: Prescot, site of a copper refmery, Trelogan, an old lead/zinc mine spoil, and Gateacre, a sewage sludge treated field. The action of zeolites to reduce available metal concentrations in soils is due to their ion-exchange properties. To investigate the decrease of metal bioavailability by zeolites, laboratory and greenhouse trials were performed to clarify the mechanism of heavy metal fixation by synthetic zeolites and to quantify the effect of different zeolites for land remediation. For this approach, it was necessary to measure the metal concentration in the soil and the soil solution in zeolite-amended soils and to determine the zeolite specific isotherms of all the metals studied. Cation exchange studies involved exchanging the sodium form of the zeolites with different metals in solution, in order to determine the zeolite affinity for the metals copper, cadmium, zinc and lead. The resulting isotherms demonstrated that all three zeolites showed a preference for the heavy metal ions over sodium ions. The changes in metal speciation in zeolite-treated contaminated soils were evaluated using sequential extraction procedures. After incubation with synthetic zeolites, metals extracted with ammonium acetate were significantly decreased (31.4 % - 72.4 %) in amended soils compared to the controls. This decrease in heavy metal availability is extremely significant. The exchangeable metal fraction is the most available for uptake by plants. Long term soil solution experiments with zeolite-amended soils showed that the metal concentrations in the aqueous leachate were significantly reduced than in the leachate from the same substrates without zeolite addition. Greenhouse pot trials were carried out with sunflower (Helianthus annuus), maize (Zea mays), willows (Salix viminalis) and ryegrass (Lolium perenne) plants grown in zeolite amended contaminated soils. There were significant improvements in visual appearance and growth of plants from the zeolite-treated soils compared to the controls. In addition, metal content of plant tissues was reduced when compared to the controls. Optimum zeolite concentrations were noted for each zeolite. Zeolite P and 4A were more effective at reducing the phytotoxicity at 0.5% and 1%, whilst zeolite Y had to be added at 5% to achieve a similar effect. In order to link the laboratory test results and soil data to a pilot field scale, in which the actual soil and environmental conditions are required to give a complete evaluation of the proposal technique when applied to a given hazardous waste site, a field trial was initiated, at a copper contaminated site at B.I.C.C., Prescot to examine the effectiveness of zeolite amendments under field conditions. Zeolites P and 4A applied at 1% level proved to be an effective treatment for the remediation of the contaminated site, as indicated by improved plant growth and low metal concentrations in the water soluble fraction of the soil. The results show that zeolite addition, particularly zeolites P and 4A, provide an effective method for decreasing plant heavy metal bioavailability in polluted soils, under glasshouse and field conditions

Explaining Vision and Language through Graphs of Events in Space and Time

Artificial Intelligence makes great advances today and starts to bridge the gap between vision and language. However, we are still far from understanding, explaining and controlling explicitly the visual content from a linguistic perspective, because we still lack a common explainable representation between the two domains. In this work we come to address this limitation and propose the Graph of Events in Space and Time (GEST), by which we can represent, create and explain, both visual and linguistic stories. We provide a theoretical justification of our model and an experimental validation, which proves that GEST can bring a solid complementary value along powerful deep learning models. In particular, GEST can help improve at the content-level the generation of videos from text, by being easily incorporated into our novel video generation engine. Additionally, by using efficient graph matching techniques, the GEST graphs can also improve the comparisons between texts at the semantic level.Comment: Accepted at IEEE International Conference on Computer Vision (ICCV) 2023 Workshops: 5th Workshop On Closing The Loop Between Vision And Languag

Analiza stărilor emoționale induse de citirea unei știri utilizând Analiza Semantică Latentă

International audienceEmoţiile pot fi identificate atât în comunicarea verbală cât şi în cea scrisă. Dacă în primul caz pot fi identificate mai ușor datorită unor trăsături specifice comunicării verbale (limbajul corpului, tonul vocii sau inflexiuni), în al doilea caz regăsirea acestora poate fi o adevărată provocare. Aşadar, propunem o metodă inedită de analiză automată a emoţiilor transmise prin intermediul comunicării scrise, mai exact, determinarea stării emoţionale a unei persoane în urma citirii unei ştiri. Cu alte cuvinte, scopul nostru este de a determina cum citirea unei ştiri afectează starea emoţională a cititorului şi să ajustăm aceste valori pe baza stării emoţionale curente a acestuia. Dintr-o perspectivă mai tehnică, sistemul dezvoltat (Emo2 – Emotions Monitor) combină o abordare independentă de context (evaluarea efectivă a ştirii utilizând tehnici de prelucrare a limbajului natural) cu influenţele determinate de starea emoţională curentă a utilizatorului. Astfel, scopul metodei propuse este de a obţine o estimare a stării emoţionale finale a utilizatorului cât mai apropiată de cea reală