A language-independent neural network for event detection

Event detection remains a challenge due to the difficulty at encoding the word semantics in various contexts. Previous approaches heavily depend on languagespecific knowledge and pre-existing natural language processing (NLP) tools. However, compared to English, not all languages have such resources and tools available. A more promising approach is to automatically learn effective features from data, without relying on languagespecific resources. In this paper, we develop a hybrid neural network to capture both sequence and chunk information from specific contexts, and use them to train an event detector for multiple languages without any manually encoded features. Experiments show that our approach can achieve robust, efficient and accurate results for multiple languages (English, Chinese and Spanish).