Text-to-Text Extraction and Verbalization of Biomedical Event Graphs

Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. Almost all contributions in the event realm orbit around semantic parsing, usually employing discriminative architectures and cumbersome multi-step pipelines limited to a small number of target interaction types. We present the first lightweight framework to solve both event extraction and event verbalization with a unified text-to-text approach, allowing us to fuse all the resources so far designed for different tasks. To this end, we present a new event graph linearization technique and release highly comprehensive event-text paired datasets, covering more than 150 event types from multiple biology subareas (English language). By streamlining parsing and generation to translations, we propose baseline transformer model results according to multiple biomedical text mining benchmarks and NLG metrics. Our extractive models achieve greater state-of-the-art performance than single-task competitors and show promising capabilities for the controlled generation of coherent natural language utterances from structured data

Learning to Predict the Stock Market Dow Jones Index Detecting and Mining Relevant Tweets

Stock market analysis is a primary interest for finance and such a challenging task that has always attracted many researchers. Historically, this task was accomplished by means of trend analysis, but in the last years text mining is emerging as a promising way to predict the stock price movements. Indeed, previous works showed not only a strong correlation between financial news and their impacts to the movements of stock prices, but also that the analysis of social network posts can help to predict them. These latest methods are mainly based on complex techniques to extract the semantic content and/or the sentiment of the social network posts. Differently, in this paper we describe a method to predict the Dow Jones Industrial Average (DJIA) price movements based on simpler mining techniques and text similarity measures, in order to detect and characterise relevant tweets that lead to increments and decrements of DJIA. Considering the high level of noise in the social network data, w e also introduce a noise detection method based on a two steps classification. We tested our method on 10 millions twitter posts spanning one year, achieving an accuracy of 88.9% in the Dow Jones daily prediction, that is, to the best our knowledge, the best result in the literature approaches based on social networks

On Deep Learning in Cross-Domain Sentiment Classification

Cross-domain sentiment classification consists in distinguishing positive and negative reviews of a target domain by using knowledge extracted and transferred from a heterogeneous source domain. Cross-domain solutions aim at overcoming the costly pre-classification of each new training set by human experts. Despite the potential business relevance of this research thread, the existing ad hoc solutions are still not scalable with real large text sets. Scalable Deep Learning techniques have been effectively applied to in-domain text classification, by training and categorising documents belonging to the same domain. This work analyses the cross-domain efficacy of a well-known unsupervised Deep Learning approach for text mining, called Paragraph Vector, comparing its performance with a method based on Markov Chain developed ad hoc for cross-domain sentiment classification. The experiments show that, once enough data is available for training, Paragraph Vector achieves accuracy equiva lent to Markov Chain both in-domain and cross-domain, despite no explicit transfer learning capability. The outcome suggests that combining Deep Learning with transfer learning techniques could be a breakthrough of ad hoc cross-domain sentiment solutions in big data scenarios. This opinion is confirmed by a really simple multi-source experiment we tried to improve transfer learning, which increases the accuracy of cross-domain sentiment classification

Enhancing Biomedical Scientific Reviews Summarization with Graph-based Factual Evidence Extracted from Papers

Combining structured knowledge and neural language models to tackle natural language processing tasks is a recent research trend that catalyzes community attention. This integration holds a lot of potential in document summarization, especially in the biomedical domain, where the jargon and the complex facts make the overarching information truly hard to interpret. In this context, graph construction via semantic parsing plays a crucial role in unambiguously capturing the most relevant parts of a document. However, current works are limited to extracting open-domain triples, failing to model real-world n-ary and nested biomedical interactions accurately. To alleviate this issue, we present EASumm, the first framework for biomedical abstractive summarization enhanced by event graph extraction (i.e., graphical representations of medical evidence learned from scientific text), relying on dual text-graph encoders. Extensive evaluations on the CDSR dataset corroborate the importance of explicit event structures, with better or comparable performance than previous state-of-the-art systems. Finally, we offer some hints to guide future research in the field

Dendritic Computation through Exploiting Resistive Memory as both Delays and Weights

Biological neurons can detect complex spatio-temporal features in spiking patterns via their synapses spread across their dendritic branches. This is achieved by modulating the efficacy of the individual synapses, and by exploiting the temporal delays of their response to input spikes, depending on their position on the dendrite. Inspired by this mechanism, we propose a neuromorphic hardware architecture equipped with multiscale dendrites, each of which has synapses with tunable weight and delay elements. Weights and delays are both implemented using Resistive Random Access Memory (RRAM). We exploit the variability in the high resistance state of RRAM to implement a distribution of delays in the millisecond range for enabling spatio-temporal detection of sensory signals. We demonstrate the validity of the approach followed with a RRAM-aware simulation of a heartbeat anomaly detection task. In particular we show that, by incorporating delays directly into the network, the network's power and memory footprint can be reduced by up to 100x compared to equivalent state-of-the-art spiking recurrent networks with no delays

Exploring the Relationship between COVID-19 Vaccine Refusal and Belief in Fake News and Conspiracy Theories: A Nationwide Cross-Sectional Study in Italy

The COVID-19 pandemic has been accompanied by an infodemic, which includes fake news (FNs) and conspiracy theories (CTs), and which may worsen vaccine refusal (VR), thus hindering the control of the transmission. This study primarily aimed to assess COVID-19 VR in Italy and its relationship with belief in FNs/CTs. Secondarily, it explored the conviction in FNs and CTs and associated variables. An online cross-sectional study was conducted in Italy (2021). The primary outcome was VR and secondary outcomes were FN misclassification score (0% to 100%: higher score means higher misclassification) and CT belief score (1 to 5: higher score means higher agreement). There were 1517 participants; 12.3% showed VR. The median FN and CT scores were: 46.7% (IQR = 40–56.7%) and 2.8 (IQR = 2.2–3.4). Age, education, FN, and CT scores had significant associations with VR. Education, economic situation, health and e-health literacy showed significant relationships with secondary outcomes. Study/work background had a significant association only with the FN score. FN and CT scores were associated. This work estimated a VR lower than before the first COVID-19 vaccine approval. The relationship between VR and FN/CT belief represents a new scenario, suggesting the need for planning effective strategies to tackle FNs and CTs to implement successful vaccination campaigns

Self-organization of an inhomogeneous memristive hardware for sequence learning

Learning is a fundamental component of creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These "technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly-set-up spiking recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware