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Page 1. EVALITA 2011 The News People Search Task: Evaluating Cross-document Coreference Resolution of Named Person Entities in Italian News L. Bentivogli, A. Marchetti, E.
Abstract. This paper describes the News People Search (NePS) Task organized as part of EVALITA 2011. The NePS Task aims at evaluating crossdocument coreference resolution of person entities in Italian news and consists of clustering a set of Italian newspaper articles that mention a person name according to the different people sharing the name. The motivation behind the task, the dataset used for the evaluation and the results obtained are described and discussed.
2008 •
Abstract This paper presents work aimed at the realization of a gold standard for cross-document coreference resolution of person entities in a corpus of Italian news. The gold standard has been created selecting a number of person names occurring in Adige-500K, a corpus composed of all the news stories published by the local newspaper" L'Adige" from 1999 to 2006. The corpus consists of 535,000 news stories, for a total of around 200 million tokens.
Proceedings iConference 2022, Virtual Event, February 28 – March 4, 2022
XCoref: Cross-document Coreference Resolution in the Wild2022 •
Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. Bridging and loose coreference relations trigger associations that may expose news readers to bias by word choice and labeling. For example, coreferential mentions of “direct talks between U.S. President Donald Trump and Kim” such as “an extraordinary meeting following months of heated rhetoric” or “great chance to solve a world problem” form a more positive perception of this event. A step towards bringing awareness of bias by word choice and labeling is the reliable resolution of coreferences with high lexical diversity. We propose an unsupervised method named XCoref, which is a CDCR method that capably resolves not only previously prevalent entities, such as persons, e.g., “Donald Trump,” but also abstractly defined concepts, such as groups of persons, “caravan of immigrants,” events and actions, e.g., “marching to the U.S. border.” In an extensive evaluation, we compare the proposed XCoref to a state-of-the-art CDCR method and a previous method TCA that resolves such complex coreference relations and find that XCoref outperforms these methods. Outperforming an established CDCR model shows that the new CDCR models need to be evaluated on semantically complex mentions with more loose coreference relations to indicate their applicability of models to resolve mentions in the “wild” of political news articles.
2008 •
Abstract In this paper we present a cross document coreference system which resolves some of the more problematic cases by taking into account pieces of evidence coming from different sources. The corpus we work with is a seven-year news collection from a local newspaper. The approach does not assume any prior knowledge about persons (eg an ontology) mentioned in the collection and requires basic linguistic processing (named entity recognition) and resources (a dictionary of person names).
Recently, many advanced machine learning approaches have been proposed for coreference resolution; however, all of the discriminatively-trained models reason over mentions rather than entities. That is, they do not explicitly contain vari- ables indicating the “canonical” values for each attribute of an entity (e.g., name, venue, title, etc.). This canonical- ization step is typically implemented as a post-processing routine to coreference resolution prior to adding the ex- tracted entity to a database. In this paper, we propose a discriminatively-trained model that jointly performs corefer- ence resolution and canonicalization, enabling features over hypothesized entities. We validate our approach on two different coreference problems: newswire anaphora resolu- tion and research paper citation matching, demonstrating im- provements in both tasks and achieving an error reduction of up to 62% when compared to a method that reasons about mentions only.
2017 •
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