Identifying event nuggets in turkish news texts using natural language processing and machine learning methods

Event nuggets are smallest textual instance that marks the existence of an event. Detecting event nuggets in a given text opens door to further research and many practical applications, therefore it has been studied extensively for some lan- guages including English, Spanish and Chinese. In this study, event nugget detection and event type classification for Turkish is studied for the first time. Due to lack of annotated data for event nugget detection in Turkish, we developed a new annotated dataset for this task. In this study we described how we manually annotated our dataset as well as our system to identify event nuggets in Turkish news texts. The dataset consists of words from Turkish news texts. Each word in the dataset is manually annotated in terms of sequence type, nugget type, realis value and whether the event nugget is the main event, thus enabling us to make analysis on this dataset for event nugget detection, event type classification, realis classification and main event detection. We made use of language specific features like morphological features and dependency parser features in Turkish as well as some other features. We aimed to see the effect of language specific features on this kind of analysis. We also experimented with different machine learning algorithms to find the best fitting model for our tasks. After having completed our experiments, we have shown that Turkish specific morphological features, dependency tree related features as well as word embeddings enabled us to achieve better results.