SciELO - Scientific Electronic Library Online

vol.17 issue2Automatic Readability Classification of Crowd-Sourced Data based on Linguistic and Information-Theoretic FeaturesAnaphora Resolution for Bengali: An Experiment with Domain Adaptation author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand




Related links

  • Have no similar articlesSimilars in SciELO


Computación y Sistemas

Print version ISSN 1405-5546


DELL'ORLETTA, Felice; VENTURI, Giulia  and  MONTEMAGNI, Simonetta. Linguistically-driven Selection of Correct Arcs for Dependency Parsing. Comp. y Sist. [online]. 2013, vol.17, n.2, pp.125-136. ISSN 1405-5546.

LISCA is an unsupervised algorithm aimed at assigning a quality score to each arc generated by a dependency parser in order to produce a decreasing ranking of arcs from correct to incorrect ones. LISCA exploits statistics about a set of linguistically-motivated and dependency-based features extracted from a large corpus of automatically parsed sentences and uses them to assign a quality score to each arc of a parsed sentence belonging to the same domain of the automatically parsed corpus. LISCA has been successfully tested on two datasets belonging to two different domains and in all experiments it turned out to outperform different baselines, thus showing to be able to reliably detect correct arcs also representing domain-specific peculiarities.

Keywords : Dependency parsing; correct arcs.

        · abstract in Spanish     · text in English     · English ( pdf )


Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License