This paper reports on two crowdsourcing experiments on Temporal Relation Annotation in Italian and English. The aim of these experiments is three-fold: first, to evaluate average Italian and English native speakers on their ability to identify and classify a temporal relation between two verbal events; second, to assess the feasibility of crowdsourcing for this kind of complex semantic task; third to perform a preliminary analysis of the role of syntax within such task. Two categories of temporal relations were investigated: relations between the main event and its subordinated event (e.g. So che hai visto Giovanni / I know you’ve seen John) and relations between two main events (e.g. Giovanni bussò ed entrò / John knocked and got in). Fifty aligned parallel sentences in the two languages from the MultiSemCor corpus were extracted. In each sentence, the source and the target verbs of the relations were highlighted and contributors were asked to select the temporal relation from 7 values (AFTER, BEFORE, INCLUDES, IS INCLUDED, SIMULTANEOUS, NO RELATION, and DON’T KNOW) inspired by the TimeML Annotation Guidelines. For each sentence, 5 judgments were collected. The results of the annotator agreement is 0.41 for Italian and 0.32 for English. Analysis of the data has shown that annotating temporal relations is not a trivial task and that dependency relations between events have a major role in facilitating the annotation. Future work aims at conducting new experiments with an additional parameter, namely factivity, and with texts in a different domain, i.e. History.
Crowdsourcing Temporal Relations in Italian and English
Sprugnoli, Rachele
2015-01-01
Abstract
This paper reports on two crowdsourcing experiments on Temporal Relation Annotation in Italian and English. The aim of these experiments is three-fold: first, to evaluate average Italian and English native speakers on their ability to identify and classify a temporal relation between two verbal events; second, to assess the feasibility of crowdsourcing for this kind of complex semantic task; third to perform a preliminary analysis of the role of syntax within such task. Two categories of temporal relations were investigated: relations between the main event and its subordinated event (e.g. So che hai visto Giovanni / I know you’ve seen John) and relations between two main events (e.g. Giovanni bussò ed entrò / John knocked and got in). Fifty aligned parallel sentences in the two languages from the MultiSemCor corpus were extracted. In each sentence, the source and the target verbs of the relations were highlighted and contributors were asked to select the temporal relation from 7 values (AFTER, BEFORE, INCLUDES, IS INCLUDED, SIMULTANEOUS, NO RELATION, and DON’T KNOW) inspired by the TimeML Annotation Guidelines. For each sentence, 5 judgments were collected. The results of the annotator agreement is 0.41 for Italian and 0.32 for English. Analysis of the data has shown that annotating temporal relations is not a trivial task and that dependency relations between events have a major role in facilitating the annotation. Future work aims at conducting new experiments with an additional parameter, namely factivity, and with texts in a different domain, i.e. History.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
