Coreference Resolution: A Key Component for Natural Language Understanding
Coreference resolution is a crucial task in natural language processing that involves identifying and linking different textual mentions that refer to the same real-world entity or concept.
In recent years, researchers have made significant progress in coreference resolution, primarily through the development of end-to-end neural network models. These models have shown impressive results on single-document coreference resolution tasks. However, challenges remain in cross-document coreference resolution, domain adaptation, and handling complex linguistic phenomena found in literature and other specialized texts.
A selection of recent research papers highlights various approaches to tackle these challenges. One study proposes an end-to-end event coreference approach (E3C) that jointly models event detection and event coreference resolution tasks. Another investigates the failures to generalize coreference resolution models across different datasets and coreference types. A third paper introduces the first end-to-end model for cross-document coreference resolution from raw text, setting a new baseline for the task.
Practical applications of coreference resolution include information retrieval, text summarization, and question-answering systems. For instance, coreference resolution can help improve the quality of automatically generated knowledge graphs, as demonstrated in a study on coreference resolution in research papers from multiple domains. Another application is in the analysis of literature, where a new dataset of coreference annotations for works of fiction has been introduced to evaluate cross-domain performance and study long-distance within-document coreference.
One company case study is the development of a neural coreference resolution system for Arabic, which substantially outperforms the existing state of the art. This system highlights the potential for coreference resolution techniques to be adapted to different languages and domains.
In conclusion, coreference resolution is a vital component of natural language understanding, with numerous practical applications and ongoing research challenges. As researchers continue to develop more advanced models and explore domain adaptation, the potential for coreference resolution to enhance various natural language processing tasks will only grow.
Coreference Resolution
Coreference Resolution Further Reading
1.End-to-End Neural Event Coreference Resolution http://arxiv.org/abs/2009.08153v1 Yaojie Lu, Hongyu Lin, Jialong Tang, Xianpei Han, Le Sun2.Investigating Failures to Generalize for Coreference Resolution Models http://arxiv.org/abs/2303.09092v1 Ian Porada, Alexandra Olteanu, Kaheer Suleman, Adam Trischler, Jackie Chi Kit Cheung3.Cross-document Coreference Resolution over Predicted Mentions http://arxiv.org/abs/2106.01210v1 Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan4.An Annotated Dataset of Coreference in English Literature http://arxiv.org/abs/1912.01140v2 David Bamman, Olivia Lewke, Anya Mansoor5.Neural Coreference Resolution for Arabic http://arxiv.org/abs/2011.00286v1 Abdulrahman Aloraini, Juntao Yu, Massimo Poesio6.Coreference Resolution in Research Papers from Multiple Domains http://arxiv.org/abs/2101.00884v1 Arthur Brack, Daniel Uwe Müller, Anett Hoppe, Ralph Ewerth7.Marmara Turkish Coreference Corpus and Coreference Resolution Baseline http://arxiv.org/abs/1706.01863v2 Peter Schüller, Kübra Cıngıllı, Ferit Tunçer, Barış Gün Sürmeli, Ayşegül Pekel, Ayşe Hande Karatay, Hacer Ezgi Karakaş8.Lexical Features in Coreference Resolution: To be Used With Caution http://arxiv.org/abs/1704.06779v1 Nafise Sadat Moosavi, Michael Strube9.Gender Bias in Coreference Resolution http://arxiv.org/abs/1804.09301v1 Rachel Rudinger, Jason Naradowsky, Brian Leonard, Benjamin Van Durme10.Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution http://arxiv.org/abs/2210.07602v1 Nupoor Gandhi, Anjalie Field, Emma StrubellCoreference Resolution Frequently Asked Questions
What is an example of coreference resolution?
Coreference resolution is the process of identifying and linking different textual mentions that refer to the same real-world entity or concept. For example, consider the following sentence: 'John went to the store, and he bought some groceries.' In this case, 'John' and 'he' refer to the same person. Coreference resolution aims to recognize that these two mentions are related and represent the same entity.
What is coreference resolution and anaphora resolution?
Coreference resolution is a natural language processing task that involves identifying and linking different textual mentions that refer to the same real-world entity or concept. Anaphora resolution is a subtask of coreference resolution that specifically deals with resolving anaphoric expressions, which are pronouns or other referring expressions that point back to a previously mentioned entity. For example, in the sentence 'Mary went to the park, and she enjoyed the weather,' the pronoun 'she' is an anaphoric expression referring to 'Mary.' Anaphora resolution aims to identify the correct antecedent for such expressions.
Why use coreference resolution?
Coreference resolution is essential for various natural language processing tasks, including information retrieval, text summarization, and question-answering systems. By resolving coreferences, these systems can better understand the relationships between different textual mentions and improve their overall performance. For instance, coreference resolution can help improve the quality of automatically generated knowledge graphs, enhance the accuracy of text summarization, and provide more precise answers in question-answering systems.
What are the different types of coreference resolution?
There are two main types of coreference resolution: single-document coreference resolution and cross-document coreference resolution. Single-document coreference resolution focuses on identifying and linking coreferential mentions within a single document, while cross-document coreference resolution aims to resolve coreferences across multiple documents. The latter is more challenging due to the increased complexity and the need to handle a larger number of textual mentions.
What are the recent advancements in coreference resolution research?
Recent advancements in coreference resolution research include the development of end-to-end neural network models, which have shown impressive results on single-document coreference resolution tasks. Researchers have also proposed new approaches to tackle challenges in cross-document coreference resolution, domain adaptation, and handling complex linguistic phenomena found in literature and other specialized texts. Some studies have introduced new datasets and benchmarks to evaluate the performance of coreference resolution models across different domains and languages.
How do neural network models contribute to coreference resolution?
Neural network models, particularly deep learning models, have significantly improved the performance of coreference resolution systems. These models can automatically learn complex patterns and relationships between textual mentions without relying on hand-crafted features or rules. End-to-end neural network models can be trained to jointly model various subtasks, such as event detection and coreference resolution, leading to better overall performance and more accurate coreference resolution.
What are the challenges in coreference resolution?
Some of the challenges in coreference resolution include cross-document coreference resolution, domain adaptation, and handling complex linguistic phenomena found in literature and other specialized texts. Cross-document coreference resolution is more challenging than single-document coreference resolution due to the increased complexity and the need to handle a larger number of textual mentions. Domain adaptation involves adapting coreference resolution models to work effectively in different domains, such as news articles, research papers, or works of fiction. Handling complex linguistic phenomena, such as long-distance within-document coreference and coreference in figurative language, also presents challenges for current coreference resolution models.
Are there any practical applications of coreference resolution in industry?
Yes, there are several practical applications of coreference resolution in industry. Some examples include information retrieval, text summarization, and question-answering systems. Coreference resolution can help improve the quality of automatically generated knowledge graphs, enhance the accuracy of text summarization, and provide more precise answers in question-answering systems. Additionally, coreference resolution techniques can be adapted to different languages and domains, as demonstrated by the development of a neural coreference resolution system for Arabic, which substantially outperforms the existing state of the art.
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