Automatic extraction of function-behaviour-state information from patents

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La leggibilità dei testi di ambito medico rivolti al paziente: il caso dei bugiardini di farmaci senza obbligo di prescrizione medica

In this paper we present the first results of an exploratory analysis of simplification of the package leaflets of medicines, considered representative texts of doctorpatient communication. It will be shown how natural language processing tools can be used to reconstruct the linguistic profile of these texts and to guide their simplification.In questo articolo presentiamo i primi risultati di un lavoro esplorativo di analisi e semplificazione dei foglietti illustrativi dei medicinali, considerati testi rappresentativi della comunicazione medicopaziente. Verrà mostrato come strumenti per il trattamento automatico del linguaggio naturale (TAL) possono essere utilizzati per ricostruire il profilo linguistico di questi testi e per guidarne la semplificazione

Combinazioni di parole e spazi semantici: un'analisi computazionale dei testi di Giordano Bruno

L’obiettivo di questo lavoro è mostrare come nuove e interessanti prospettive di indagine possano venire dall’applicazione all’analisi dei testi filosofici di metodi avanzati di linguistica computazionale e di rappresentazione del significato basati sulla costruzione automatica di spazi di similarità semantica determinati dalle modalità con cui le parole si distribuiscono e si combinano in un testo. A tale scopo, verranno illustrati i risultati di alcuni esperimenti condotti sugli Eroici Furori (1585) di Giordano Bruno, mostrando come le tecniche di semantica computazionale permettano di cogliere aspetti interessanti delle dinamiche del senso che si realizzano nel testo e che possono costituire tracce utili all’esplorazione del pensiero bruniano

Chunking and Dependency Parsing

Since chunking can be performed efficiently and accurately, it is attractive to use it as a preprocessing step in full parsing stages. We analyze whether providing chunk data to a statistical dependency parser can benefit its accuracy. We present a set of experiments meant to select first a set of features that provide the greates improvement to a Shift/Reduce dependency parser, then to determine an appropriate feature model. We report on accuracy gain obtained using features from chunks produced using a statistical chunker as well as from an approximate representation of noun phrases induced directly by the parser. Finally we analyze the degree of accuracy that such a parser can achieve in chunking compared to a specialized statistical chunker

Reverse Revision and Linear Tree Combination for Dependency Parsing

We propose a parsing method that allows reducing several of these errors, although maintaining a quasi linear complexity. The method consists in two steps: first the sentence is parsed by a deterministic Shift/Reduce parser, then a second deterministic Shift/Reduce parser analyzes the sentence in reverse using additional features extracted from the parse trees produced by the first parser. We introduce an alternative linear combination method. The algorithm is greedy and works by combining the trees top down. The experiments show that in practice its output often outperforms the results produced by calculating the MST

Corpus-based Modelling of Grammar Variation

Current research in natural language learning and processing supports the view that grammatical competence consists in mastering and integrating multiple, parallel “constraints” (Seidenberg and MacDonald 1999, MacWhinney 2004, Burzio 2005). During language comprehension, constraints are defined as a set of cues through which a speaker can successfully map a complex linguistic unit (a word form, a phrase, an utterance etc.) to its intended function in discourse. For example, in the Italian sentence arriva il treno (‘the train is approaching’), the noun phrase il treno is understood to play the role of subject due to an integrated cluster of cues concerning the position of the subject relative to the verb, noun-verb agreement, compliance of predicate selectional restrictions and general knowledge about the world. Conversely, in language production, constraints consist in discourse functions that are jointly mapped onto linguistic forms. If we want to translate arriva il treno into English, we have to be aware of the peculiar functional constraint positioning the subject before the verb in the target language

DeSRL: A Linear-Time Semantic Role Labeling System

This paper describes the DeSRL system, a joined effort of Yahoo! Research Barcelona and Università di Pisa for the CoNLL-2008 Shared Task (Surdeanu et al., 2008). The system is characterized by an efficient pipeline of linear complexity components, each carrying out a different sub-task. Classifier errors and ambiguities are addressed with several strategies: revision models, voting, and reranking. The system participated in the closed challenge ranking third in the complete problem evaluation with the following scores: 82.06 labeled macro F1 for the overall task, 86.6 labeled attachment for syntactic dependencies, and 77.5 labeled F1 for semantic dependencies

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 mes- sages for disaster management and aims at the detection of messages carrying critical information for the damage as- sessment 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 fea- tures 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 investi- gated the most effective features that allow to achieve the best results. A further result of this study is the construc- tion of the first manually annotated Italian corpus of social media messages for damage assessment