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

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

Accurate Dependency Parsing with a Stacked Multilayer Perceptron

DeSR is a statistical transition-based dependency parser which learns from annotated corpora which actions to perform for building parse trees while scanning a sentence. We describe recent improvements to the parser, in particular stacked parsing, exploiting a beam search strategy and using a Multilayer Perceptron classifier. For the Evalita 2009 Dependency Parsing task DesR was configured to use a combination of stacked parsers. The stacked combination achieved the best accuracy scores in both the main and pilot subtasks. The contribution to the result of various choices is analyzed, in particular for taking advantage of the peculiar features of the TUT Treebank. Keywords: parser, dependency parsing, perceptron, classifier, natural language

Probing the Space of Grammatical Variation: Induction of Cross-Lingual Grammatical Constraints from Treebanks

quantitative analysis of distributional language data of both Italian and Czech, highlighting the relative contribution of a number of distributed grammatical factors to sentence-based identification of subjects and direct objects. The work uses a Maximum Entropy model of stochastic resolution of conflicting grammatical constraints and is demonstrably capable of putting explanatory theoretical accounts to the test of usage-based empirical verification