An Automatic Intelligent System for Document Processing and Fruition

With the increasing amount of documents available on-line, the need for intelligent digital libraries, that allow to automatize the document processing tasks and to suitably organize and make available the documents so as to provide personalized and focused access, becomes more and more pressing. This paper proposes an integrated system that merges intelligent modules covering all the phases involved in a document lifecycle, from acquisition, to processing, to information extraction, to personalized fruition for final users. The role and possible cooperation of Machine Learning and Data Mining techniques in the system is highlighted and discussed, along with their importance to provide effective support to both the building and the fruition of the Digital Library and the underlying knowledge base

Modelling and Searching of Combinatorial Spaces Based on Markov Logic Networks

Markov Logic Networks (MLNs) combine Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. Learning the structure of MLNs is performed by state-of-the-art methods by maximizing the likelihood of a relational database. This leads to suboptimal results for prediction tasks due to the mismatch between the objective function (likelihood) and the task of classification (maximizing conditional likelihood (CL)). In this paper we propose two algorithms for learning the structure of MLNs. The first maximizes the CL of query predicates instead of the joint likelihood of all predicates while the other maximizes the area under the Precision-Recall curve (AUC). Both algorithms set the parameters by maximum likelihood and choose structures by maximizing CL or AUC. For each of these algorithms we develop two different searching strategies. The first is based on Iterated Local Search and the second on Greedy Randomized Adaptive Search Procedure. We compare the performances of these randomized search approaches on real-world datasets and show that on larger datasets, the ILS-based approaches perform better, both in terms of CLL and AUC, while on small datasets, ILS and RBS approaches are competitive and RBS can also lead to better results for AUC

UniBA @ KIPoS: A Hybrid Approach for Part-of-Speech Tagging

The Part of Speech tagging operation is becoming increasingly important as it represents the starting point for other high-level operations such as Speech Recognition, Machine Translation, Parsing and Information Retrieval. Although the accuracy of state-of-the-art POS-taggers reach a high level of accuracy (around 96-97%) it cannot yet be considered a solved problem because there are many variables to take into account. For example, most of these systems use lexical knowledge to assign a tag to unknown words. The task solution proposed in this work is based on a hybrid tagger, which doesn’t use any prior lexical knowledge, consisting of two different types of POS-taggers used sequentially: HMM tagger and RDRPOSTagger [(Nguyen et al., 2014), (Nguyen et al., 2016)]. We trained the hybrid model using the Development set and the combination of Development and Silver sets. The results have shown an accuracy of 0,8114 and 0,8100 respectively for the main task.L’operazione di Part of Speech tagging sta diventando sempre più importante in quanto rappresenta il punto di partenza per altre operazioni di alto livello come Speech Recognition, Machine Translation, Parsing e Information Retrieval. Sebbene l’accuratezza dei POS tagger allo stato dell’arte raggiunga un alto livello di accuratezza (intorno al 96-97%), esso non può ancora essere considerato un problema risolto perché ci sono molte variabili da tenere in considerazione. Ad esempio, la maggior parte di questi sistemi utilizza della conoscenza linguistica per assegnare un tag alle parole sconosciute. La soluzione proposta in questo lavoro si basa su un tagger ibrido, che non utilizza alcuna conoscenza linguistica pregressa, costituito da due diversi tipi di POS-tagger usati in sequenza: HMM tagger e RDRPOSTagger [(Nguyen et al., 2014), (Nguyen et al., 2016)]. Abbiamo addestrato il modello ibrido utilizzando il Development Set e la combinazione di Silver e Development Sets. I risultati hanno mostrato un’accuratezza pari a 0,8114 e 0,8100 rispettivamente per il task main

An abstract argumentation approach for the prediction of analysts’ recommendations following earnings conference calls

Financial analysts constitute an important element of financial decision-making in stock exchanges throughout the world. By leveraging on argumentative reasoning, we develop a method to predict financial analysts' recommendations in earnings conference calls (ECCs), an important type of financial communication. We elaborate an analysis to select those reliable arguments in the Questions Answers (QA) part of ECCs that analysts evaluate to estimate their recommendation. The observation date of stock recommendation update may variate during the next quarter: it can be either the day after the ECC or it can take weeks. Our objective is to anticipate analysts' recommendations by predicting their judgment with the help of abstract argumentation. In this paper, we devise our approach to the analysis of ECCs, by designing a general processing framework which combines natural language processing along with abstract argumentation evaluation techniques to produce a final scoring function, representing the analysts' prediction about the company's trend. Then, we evaluate the performance of our approach by specifying a strategy to predict analysts recommendations starting from the evaluation of the argumentation graph properly instantiated from an ECC transcript. We also provide the experimental setting in which we perform the predictions of recommendations as a machine learning classification task. The method is shown to outperform approaches based only on sentiment analysis

編集後記、執筆者紹介、奥付

This paper outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. The aim of this work is to define a general systematic method to verify the effective collaboration among the members of a team and to compare the different multi-agent behaviours, using external observations of a Multi-Agent System. Observing and analysing the behavior of a such system is a difficult task. Our approach allows to learn sequential behaviours from raw multi-agent observations of a dynamic, complex environment, represented by a set of sequences expressed in first-order logic. In order to discover the underlying knowledge to characterise team behaviours, we propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences. We compared the performance of two soccer teams in a simulated environment, each based on very different behavioural approaches: While one uses a more deliberative strategy, the other one uses a pure reactive one

Case report:A pediatric case of Bickerstaff brainstem encephalitis after COVID-19 vaccination and Mycoplasma pneumoniae infection: Looking for the culprit

Bickerstaff brainstem encephalitis (BBE) is a rare, immune-mediated disease characterized by the acute onset of external ophthalmoplegia, ataxia, and consciousness disturbance. It has a complex multifactorial etiology, and a preceding infectious illness is seen in the majority of cases. Immune-mediated neurological syndromes following COVID-19 vaccination have been increasingly described. Here we report the case of a child developing BBE 2 weeks after COVID-19 vaccination. Despite nerve conduction studies and CSF analysis showing normal results, BBE was diagnosed on clinical ground and immunotherapy was started early with a complete recovery. Later, diagnosis was confirmed by positive anti-GQ1b IgG in serum. Even if there was a close temporal relationship between disease onset and COVID-19 vaccination, our patient also had evidence of a recent Mycoplasma pneumoniae infection that is associated with BBE. Indeed, the similarity between bacterial glycolipids and human myelin glycolipids, including gangliosides, could lead to an aberrantly immune activation against self-antigens (i.e., molecular mimicry). We considered the recent Mycoplasma pneumoniae infection a more plausible explanation of the disease onset. Our case report suggests that suspect cases of side effects related to COVID-19 vaccines need a careful evaluation in order to rule out well-known associated factors before claiming for a causal relationship