Titlebook: Evaluation of Natural Language and Speech Tool for Italian; International Worksh Bernardo Magnini,Francesco Cutugno,Emanuele Pianta Confere

諄諄教誨

A Simple Yet Effective Approach for Named Entity Recognition from Transcribed Broadcast Newspproach for NER on speech transcriptions which achieves good results despite the peculiarities. The novelty of our approach is that it emphasizes on the maximum exploitation of the tokens, as they are, in the data. We developed a system for participating in the “NER on Transcribed Broadcast News” (c

失誤

A Combination of Classifiers for Named Entity Recognition on Transcriptionat the output of one of the classifiers is exploited by the other to refine its decision. The approach we followed is similar to that used in Typhoon, which is a NER system designed for newspaper articles; in that respect, one of the distinguishing features of our approach is the use of Conditional

nocturia

The Tanl Tagger for Named Entity Recognition on Transcribed Broadcast News at Evalita 2011eatures according to feature templates expressed through patterns provided in a configuration file. The Tanl Tagger was applied to the task of Named Entity Recognition (NER) on Transcribed Broadcast News of Evalita 2011. The goal of the task was to identify named entities within texts produced by an

BAIL

gregarious

Evalita 2011: Anaphora Resolution Taskbe seen as a successor of the Evalita-2009 Local Entity Detection and Recognition (LEDR) track, expanding on the scope of addressed phenomena. The annotation guidelines have been designed to cover a large variety of linguistic issues related to anaphora/coreference..We describe the annotation scheme

上釉彩

UNIPI Participation in the Evalita 2011 Anaphora Resolution Task entity within a given document. The UNIPI system is based on the analysis of dependency parse trees and on similarity clustering. Mention detection relies on parse trees obtained by re-parsing texts with DeSR, and on ad-hoc heuristics to deal with specific cases, when mentions boundaries do not cor

INTER

拉開這車床

Super-Sense Tagging Using Support Vector Machines and Distributional Featuresper-sense that defines a general concept such as ., . or .. Due to the smaller set of concepts involved the task is simpler than Word Sense Disambiguation one which identifies a specific meaning for each word. In this task, we exploit a supervised learning method based on Support Vector Machines. Ho

繁榮中國

西瓜