Tale of tails using rule augmented sequence labeling for event extraction
Table 1: InDEE-2019 dataset for five languages, namely, Marathi, Hindi, English, Tamil and Bengali. Number of tags or labels for each dataset and their respective train, validation and test split used in the experiments.
['Languages', 'Marathi(Mr) Doc', 'Marathi(Mr) Sen', 'Hindi(Hi) Doc', 'Hindi(Hi) Sen', 'English(En) Doc', 'English(En) Sen', 'Tamil(Ta) Doc', 'Tamil(Ta) Sen', 'Bengali(Bn) Doc', 'Bengali(Bn) Sen']
We focus on the EE task and model as a sequence labeling problem. We use the following terminologies throughout the paper: Event Trigger : The main word that identifies occurrence of the event mentioned in the document. Event Arguments : Several words that define an events such as place , time , reason , after-effects , participant , casualties. We are interested in extracting event triggers, event arguments from the document. We release the new dataset named InDEE-2019 which consists of tagged event extraction data in disaster domain covering five languages: Marathi, English, Hindi, Bengali and Tamil. We train our marathi model on 15.9K sentences and predict on 43 labels. We train our model on varying training set size, namely, 20%, 40%, 60%, 80% and 100% to ascertain the impact of rules with decreasing amount of dataset. It is observed that at lesser training instances, our rule based approach is able to outperform baseline on 3K(20%) and 6K(40%) training instances. Both macro and micro F1 scores show improvement over baseline model. As training instances increases, deep learning models are able to learn large number of parameters that were difficult to learn on lesser training instances.