Guillermo Moncecchi

Event annotation schemes and event recognition in spanish texts

This paper presents an annotation scheme for events in Spanish texts, based on TimeML for English. This scheme is contrasted with different proposals, all of them based on TimeML, for various Romance languages: Italian, French and Spanish. Two manually annotated corpora for Spanish, under the proposed scheme, are now available. While manual annotation is far from trivial, we obtained a very good event identification agreement (93% of events were identically identified by both annotators). Part of the annotated text was used as a training corpus for the automatic recognition of events. In the experiments conducted so far (SVM and CRF) our best results are in the state of the art for this task (80.3% of F-measure).

Is This a Joke? Detecting Humor in Spanish Tweets

While humor has been historically studied from a psychological, cognitive and linguistic standpoint, its study from a computational perspective is an area yet to be explored in Computational Linguistics. There exist some previous works, but a characterization of humor that allows its automatic recognition and generation is far from being specified. In this work we build a crowdsourced corpus of labeled tweets, annotated according to its humor value, letting the annotators subjectively decide which are humorous. A humor classifier for Spanish tweets is assembled based on supervised learning, reaching a precision of 84 % and a recall of 69 %.

The Influence of Syntactic Information on Hedge Scope Detection

In this paper we elaborate over the use of sequential supervised learning methods on the task of hedge cue scope detection. We address the task using a learning methodology that proposes the use of an iterative, error-based approach to improve classification performance. We analyze how the incorporation of syntactic constituent information to the learning and post-processing steps produces a performance improvement of almost twelve points in terms of F-score over previously unseen data.

Lavinia: An Environment for Natural Language Processing

En este artículo presentamos Lavinia, un ambiente para Procesamiento de Lenguaje Natural (PLN), en donde desarrolladores y usuarios pueden integrar y compartir componentes construidos en la plataforma UIMA. Laviniaintroduce un algoritmo para visualizar los resultados independiente del proceso que los generó, permitiendo además al usuario modificar en forma dinámica la forma en que se muestran, buscando destacar aquellos aspectos del análisis que sean de su interés.