Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language

We present a comparison between deep learning and traditional machine learning methods for various NLP tasks in Italian. We carried on experiments using available datasets (e.g., from the Evalita shared tasks) on two sequence tagging tasks (i.e., named entities recognition and nominal entities recognition) and four classification tasks (i.e., lexical relations among words, semantic relations among sentences, sentiment analysis and text classification). We show that deep learning approaches outperform traditional machine learning algorithms in sequence tagging, while for classification tasks that heavily rely on semantics approaches based on feature engineering are still competitive. We think that a similar analysis could be carried out for other languages to provide an assessment of machine learning / deep learning models across different languages

Identifying the emerging vulnerability of railway transport systems across countries by automated analysis of railway accident reports

Although railway accident reports and recommendations are proposed after railway accidents, practitioners and researchers suffer from the need to deal with a large amount of textual data given that most railway safety-related information is recorded and stored in the form of text. Hence, there is a growing need for accurate estimations of the vulnerability of railway transport and for effective mitigation strategies. This thesis extends knowledge on the vulnerability of the railway system by exploring the underlying hazards and building rigorous and automated models to enlarge the database. The conceptual frameworks HazardMap and RecoMap were developed to overcome this gap, using Natural Language Processing (NLP) topic models for the automated analysis of textual data to extract critical insights. Empirical data was retrieved from official railway accident reports published by four countries: Australia - the Australian Transport Safety Bureau (ATSB), the UK - Rail Accident Investigation Branch (RAIB), the US - National Transportation Safety Board (NTSB) and Canada - the Transportation Safety Board of Canada (TSB). Scoping workshops and a survey were conducted to evaluate the usefulness and consistency of railway practice. Case studies of the application to the risk at level crossings and the platform–train interface risks are provided to illustrate how the models proposed work with real-world data. The interpretation of findings indicates the potentially emerging hazard of deterioration in railway safety. Potential barriers to learning across jurisdictions and time might deteriorate the organisational safety culture and endanger railway. To address such obstacles, the HazardMap and RecoMap proposed are capable of automating hazard analysis with adequate accuracy to help stakeholders better understand hazards and help practitioners learn across jurisdictions and time