Andrea Cimino

A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages

This work focuses on the analysis of Italian social media messages for disaster management and aims at the detection of messages carrying critical information for the damage assessment task. A main novelty of this study consists in the focus on out-domain and cross-event damage detection, and on the investigation of the most relevant tweet-derived features for these tasks. We devised different experiments by resorting to a wide set of linguistic features qualifying the lexical and grammatical structure of a text as well as ad-hoc features specifically implemented for this task. We investigated the most effective features that allow to achieve the best results. A further result of this study is the construction of the first manually annotated Italian corpus of social media messages for damage assessment.

Crisis Mapping During Natural Disasters via Text Analysis of Social Media Messages

Recent disasters demonstrated the central role of social media during emergencies thus motivating the exploitation of such data for crisis mapping. We propose a crisis mapping system that addresses limitations of current state-of-the-art approaches by analyzing the textual content of disaster reports from a twofold perspective. A damage detection component employs a SVM classifier to detect mentions of damage among emergency reports. A novel geoparsing technique is proposed and used to perform message geolocation. We report on a case study to show how the information extracted through damage detection and message geolocation can be combined to produce accurate crisis maps. Our crisis maps clearly detect both highly and lightly damaged areas, thus opening up the possibility to prioritize rescue efforts where they are most needed.

Assessing the Readability of Sentences: Which Corpora and Features?

The paper investigates the problem of sentence readability assessment, which is modelled as a classification task, with a specific view to text simplification. In particular, it addresses two open issues connected with it, i.e. the corpora to be used for training, and the identification of the most effective features to determine sentence readability. An existing readability assessment tool developed for Italian was specialized at the level of training corpus and learning algorithm. A maximum entropy–based feature selection and ranking algorithm (grafting) was used to identify to the most relevant features: it turned out that assessing the readability of sentences is a complex task, requiring a high number of features, mainly syntactic ones.

PaCCSS-IT: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification.

In this paper we present PaCCSS–IT, a Parallel Corpus of Complex–Simple Sentences for ITalian. To build the resource we develop a new method for automatically acquiring a corpus of complex–simple paired sentences able to intercept structural transformations and particularly suitable for text simplification. The method requires a wide amount of texts that can be easily extracted from the web making it suitable also for less–resourced languages. We test it on the Italian language making available the biggest Italian corpus for automatic text simplification.