Event detection in Hungarian texts with dependency and constituency parsing and WordNet

Besides named entity recognition, the detection of events in natural language is an important area of Information Extraction. The detection and analysis of events in natural language texts play an important role in several NLP applications such as summarization and question answering. Most events are denoted by verbs in texts and the verbs usually denote events. But other parts of speech (e.g. noun, participle) can also denote events. In our work we deal with the detection of verbal, infinitival and nominal events. These are the most common events. In this study we introduce a machine learning-based approach with a rich feature set that can automatically detect events in Hungarian texts based on dependency and constituency parsing and WordNet. Additional methods were also applied beside the features, which improved the results and decreased running time. To our best knowledge, ours is the first result for detecting events in Hungarian natural language texts, with dependency and constituency parsing and WordNet. Having evaluated them on test databases, our algorithms achieve competitive results as compared to the current results.