Abstract: A machine accesses a preexisting set of natural language text documents in multiple natural languages. Each natural language text document in at least a portion of the preexisting set is associated with an event. The machine trains, using the preexisting set of natural language text documents and the associated events, an event encoder to learn associations between texts and event annotations. The event encoder leverages a parser in each of the two or more natural languages. The machine generates, using the event encoder, new event annotations for texts. The machine trains, using the preexisting set of natural language text documents and the new event annotations for the texts generated by the event encoder, an event extraction engine to extract events from natural language texts in the two or more natural languages. The event extraction engine leverages the parser in each of the two or more natural languages.

Abstract: A machine accesses a query in a first natural language. The machine identifies an event corresponding to the query. The machine computes, using a cross-lingual information retrieval module, a ranked list of documents in a second natural language that are related to the event. At least a portion of documents in the ranked list are selected from a collection of documents in the second natural language that are not annotated with events. The cross-lingual information retrieval module is trained using a dataset comprising annotated documents in the first natural language and translations of the annotated documents into the second natural language. Each annotated document is annotated with one or more events. The machine provides an output representing at least a portion of the ranked list of documents in the second natural language. The second natural language is different from the first natural language.

Abstract: A machine accesses a preexisting set of natural language text documents in multiple natural languages. Each natural language text document in at least a portion of the preexisting set is associated with an event. The machine trains, using the preexisting set of natural language text documents and the associated events, an event encoder to learn associations between texts and event annotations. The event encoder leverages a parser in each of the two or more natural languages. The machine generates, using the event encoder, new event annotations for texts. The machine trains, using the preexisting set of natural language text documents and the new event annotations for the texts generated by the event encoder, an event extraction engine to extract events from natural language texts in the two or more natural languages. The event extraction engine leverages the parser in each of the two or more natural languages.

Abstract: A machine accesses a preexisting set of natural language text documents in multiple natural languages. Each natural language text document in at least a portion of the preexisting set is associated with an event. The machine trains, using the preexisting set of natural language text documents and the associated events, an event encoder to learn associations between texts and event annotations. The event encoder leverages a parser in each of the two or more natural languages. The machine generates, using the event encoder, new event annotations for texts. The machine trains, using the preexisting set of natural language text documents and the new event annotations for the texts generated by the event encoder, an event extraction engine to extract events from natural language texts in the two or more natural languages. The event extraction engine leverages the parser in each of the two or more natural languages.

Abstract: A machine accesses a preexisting set of natural language text documents in multiple natural languages. Each natural language text document in at least a portion of the preexisting set is associated with an event. The machine trains, using the preexisting set of natural language text documents and the associated events, an event encoder to learn associations between texts and event annotations. The event encoder leverages a parser in each of the two or more natural languages. The machine generates, using the event encoder, new event annotations for texts. The machine trains, using the preexisting set of natural language text documents and the new event annotations for the texts generated by the event encoder, an event extraction engine to extract events from natural language texts in the two or more natural languages. The event extraction engine leverages the parser in each of the two or more natural languages.

Abstract: A machine accesses a query in a first natural language. The machine identifies an event corresponding to the query. The machine computes, using a cross-lingual information retrieval module, a ranked list of documents in a second natural language that are related to the event. At least a portion of documents in the ranked list are selected from a collection of documents in the second natural language that are not annotated with events. The cross-lingual information retrieval module is trained using a dataset comprising annotated documents in the first natural language and translations of the annotated documents into the second natural language. Each annotated document is annotated with one or more events. The machine provides an output representing at least a portion of the ranked list of documents in the second natural language. The second natural language is different from the first natural language.