By Cristina Bosco, Alessandro Mazzei (auth.), Bernardo Magnini, Francesco Cutugno, Mauro Falcone, Emanuele Pianta (eds.)
EVALITA (http://www.evalita.it/) is the reference review crusade of either common Language Processing and Speech applied sciences for the Italian language. the target of the shared initiatives proposed at EVALITA is to advertise the advance of language applied sciences for Italian, offering a typical framework the place diverse structures and techniques could be evaluated and in comparison in a constant demeanour. This quantity collects the ultimate and prolonged contributions provided at EVALITA 2011, the 3rd version of the evaluate crusade. The 36 revised complete papers have been conscientiously reviewed and chosen from a complete of 87 submissions. The papers are prepared in topical sections approximately equivalent to review projects: parsing - dependency parsing tune, parsing - constituency parsing tune, area edition for dependency parsing, named entity attractiveness on transcribed broadcast information, cross-document coreference solution of named individual entities, anaphora answer, supersense tagging, body labeling over italian texts, lemmatisation, automated speech popularity - huge vocabulary transcription, pressured alignment on spontaneous speech.
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Extra info for Evaluation of Natural Language and Speech Tools for Italian: International Workshop, EVALITA 2011, Rome, January 24-25, 2012, Revised Selected Papers
Table 1 shows the results of different parsing algorithms implemented in MaltParser using 10-fold cross validation on the TUT training set. Table 2 reports the results of different parsing algorithms implemented in MaltParser on the TUT test set. The results of the two best performing systems at EVALITA 2009 are reported too. Note that the best performing 1 2 We thank Joakim Nivre and Mihai Surdeanu for making their parsers available and for kindly answering our questions about their usage. org/ B.
However “bozza” is a child of “una”, which is a child of “cioè”, which is a child of comma, which is a child of “draft”. Hence, in order to figure out that the comma is a way to relate “draft” with “bozza” it would have to look ahead 4 tokens and be sure that the intervening tokens do not relate to something else. In order to handle this problem we have experimented with a variant of the parsing algorithm that delays Left reductions until the phrases on the right have been parsed. This requires also introducing an UnShift operation, in order to resume the Left reduction at the proper time.
Transition Systems. MaltParser has four built-in transition systems: – – – – Nivre’s arc-eager system  Nivre’s arc-standard system  Covington’s projective system  Covington’s non-projective system  The two versions of Nivre’s transition system are inspired by shift-reduce parsing and use a single stack to store partially processed words. The only difference is that the arc-standard version builds trees strictly bottom-up, while the arc-eager version builds structure incrementally from left to right.