Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events

We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event chains. Among event mentions in the same chain, we distinguish within- and cross-document event coreference links by using two distinct pairwise classifiers, trained separately to capture differences in feature distributions of within- and cross-document event clusters. Our event coreference approach alternates between WD and CD clustering and combines arguments from both event clusters after every merge, continuing till no more merge can be made. And then it performs further merging between event chains that are both closely related to a set of other chains of events. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods in joint task of WD and CD event coreference resolution.Comment: EMNLP 201

Identity and Granularity of Events in Text

In this paper we describe a method to detect event descrip- tions in different news articles and to model the semantics of events and their components using RDF representations. We compare these descriptions to solve a cross-document event coreference task. Our com- ponent approach to event semantics defines identity and granularity of events at different levels. It performs close to state-of-the-art approaches on the cross-document event coreference task, while outperforming other works when assuming similar quality of event detection. We demonstrate how granularity and identity are interconnected and we discuss how se- mantic anomaly could be used to define differences between coreference, subevent and topical relations.Comment: Invited keynote speech by Piek Vossen at Cicling 201

Dynamic Entity Representations in Neural Language Models

Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their mentions. Our model is generative and flexible; it can model an arbitrary number of entities in context while generating each entity mention at an arbitrary length. In addition, it can be used for several different tasks such as language modeling, coreference resolution, and entity prediction. Experimental results with all these tasks demonstrate that our model consistently outperforms strong baselines and prior work.Comment: EMNLP 2017 camera-ready versio

Ordenación de eventos multidocumento usando inferencia de relaciones temporales y modelos semánticos distribucionales

This paper focuses on the contribution of temporal relations inference and distributional semantic models to the event ordering task. Our system automatically builds ordered timelines of events from different written texts in English by performing first temporal clustering and then semantic clustering. In order to determine temporal compatibility, an inference from the temporal relationships between events –automatically extracted from a Temporal Information Processing system– is applied. Regarding semantic compatibility between events, we analyze two different distributional semantic models: LDA Topic modeling and Word2Vec word embeddings. Both semantic models together with the temporal inference have been evaluated within the framework of SemEval 2015 Task 4 Track B. Experiments show that, using both models, the current State of the Art is improved, showing significant advance in the Cross-Document Event Ordering task.Este artículo se centra en estudiar la contribución que la inferencia de relaciones temporales y los modelos semánticos distribucionales hacen a la tarea de ordenación de eventos. Nuestro sistema construye automáticamente líneas de tiempo con eventos extraídos de diferentes documentos escritos en inglés. Para ello realiza primero una agrupación temporal y posteriormente una agrupación semántica. Para determinar la compatibilidad temporal se realiza una inferencia sobre las relaciones temporales entre los eventos extraídos de un sistema automático de procesamiento de información temporal. Para la compatibilidad semántica entre eventos hemos analizado dos modelos semánticos distribucionales distintos: LDA Topic Modeling y Word2Vec Word Embeddings. Ambos modelos semánticos junto con la inferencia temporal han sido evaluados bajo el marco de evaluación de SemEval 2015 Task 4 Track B. Los experimentos muestran que, usando ambos modelos se mejora el estado del arte actual, implicando un avance importante en la tarea de ordenación de eventos multidocumento.This paper has been partially supported by the Spanish government, project TIN2015-65100-R, project TIN2015-65136-C2-2-R and PROMETEOII/2014/001