Joint Coreference Resolution and Named-Entity Linking with Multi-Pass Sieves

By Hannaneh Hajishirzi, Leila Zilles, Daniel S. Weld, Luke S. Zettlemoyer. 2013

87 citations recorded by the source corpus.

Many errors in coreference resolution come from semantic mismatches due to inadequate world knowledge. Errors in named-entity linking (NEL), on the other hand, are often caused by superficial modeling of entity context. This paper demonstrates that these two tasks are complementary. We introduce NECO, a new model for named entity linking and coreference resolution, which solves both problems jointly, reducing the errors made on each. NECO extends the Stanford deterministic coreference system by automatically linking mentions to Wikipedia and introducing new NEL-informed mention-merging sieves. Linking improves mention-detection and enables new semantic attributes to be incorporated from Freebase, while coreference provides better context modeling by propagating named-entity links within mention clusters. Experiments show consistent improvements across a number of datasets and experimental conditions, including over 11% reduction in MUC coreference error and nearly 21% reduction in F1 NEL error on ACE 2004 newswire data.

Subfields: Artificial Intelligence, Management Science and Operations Research, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Management Science and Operations Research

In Natural Language Processing

Citations in the corpus

Learning to Solve Arithmetic Word Problems with Verb Categorization (2014, methodology): “ARIS applies the Stanford dependency parser, named entity recognizer and coreference resolution system to the problem text (de Marneffe et al., 2006; Finkel et al., 2005; Raghunathan et al., 2010; Hajishirzi et al., 2013).”
Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation (2015, background): “, 2013) and coreference and entity linking (Hajishirzi et al., 2013; Dutta and Weikum, 2015).”
A Joint Model for Entity Analysis: Coreference, Typing, and Linking (2014, methodology): “shown that tighter integration of coreference and entity linking is promising (Hajishirzi et al., 2013; Zheng et al., 2013); we extend these approaches and model the entire process more holistically.”
Design Challenges for Entity Linking (2015, methodology): “…relation extraction (Craven and Kumlien, 1999; Riedel et al., 2010; Hoffmann et al., 2011; Koch et al., 2014) that uses EL to create training data, and some coreference systems that use EL for disambiguation (Hajishirzi et al., 2013; Zheng et al., 2013; Durrett and Klein, 2014).”
A general framework for information extraction using dynamic span graphs (2019, methodology): “, 2017), entities and coreferences (Hajishirzi et al., 2013; Durrett and Klein, 2014), joint inference (Singh et al.”
Constrained Semi-supervised Learning in the Presence of Unanticipated Classes (2016, background): “Advanced EL methods consider multiple entity mentions in a document collectively in order to enrich the contextual information that is modeled per entity [58, 134].”
Entity Linking via Joint Encoding of Types, Descriptions, and Context (2017, background): “Few approaches jointly model entity linking, and other related NLP tasks to improve linking, such as, coreference resolution (Hajishirzi et al., 2013), relational inference (Cheng and Roth, 2013), and joint coreference with typing (Durrett and Klein, 2014).”
MMKG: Multi-Modal Knowledge Graphs (2019, unknown): “r hypothesis about the need of such data set. Finally, Section 6 presents our conclusions. 2 Relevance There are a number of problems related to knowledge graph completion. Named-entity linking (NEL) [7,12] is the task of linking a named-entity mention from a text to an entity in a knowledge graph. ”
Learning Distributed Representations of Texts and Entities from Knowledge Base (2017, background): “Entity Linking (EL) (Cucerzan, 2007; Mihalcea and Csomai, 2007; Milne and Witten, 2008; Ratinov et al., 2011; Hajishirzi et al., 2013; Ling et al., 2015) is the task of resolving ambiguous mentions of entities to their referent entities in KB.”
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers (2018, background): “NECo is proposed in [75] and capable of solving both named entity linking and co-reference resolution jointly.”