Journal of Natural Language Engineering: Special Issue on Knowledge-Rich Coreference Resolution

Event Notification Type: 
Call for Papers
Contact: 
Maciej Ogrodniczuk
Vincent Ng
Submission Deadline: 
Sunday, 15 January 2017

Call for Papers

Coreference resolution, the task of determining the mentions in a text or dialogue that refer to the same entity in the real world, has been at the core of natural language understanding since the 1960s. Owing in large part to the public availability of several coreference-annotated corpora since the 1990s, such as MUC, ACE, and OntoNotes, significant progress has been made in the development of corpus-based approaches to coreference resolution.

Advances in modeling have outpaced advances in feature engineering for coreference resolution, however. While the development of large lexical databases (e.g., WordNet, Wikipedia, FrameNet, YAGO, and Freebase) and the progress made in corpus-based lexical semantics research in the past 15 years have enabled researchers to automatically extract and employ sophisticated knowledge for coreference resolution, Durrett and Klein (2013) have shown that the addition of shallow semantic features to the morpho-syntactic feature set employed by their state-of-the-art resolver failed to improve its performance. Nevertheless, recent results suggest that the performance of knowledge-lean coreference resolvers is plateauing, and that performance gains beyond the current state of the art will likely come from the incorporation of sophisticated knowledge sources.

To encourage work on advancing the state of the art in coreference resolution using sophisticated knowledge sources, we invite contributions on topics related to knowledge-rich coreference resolution, including but not limited to the following areas:

Employing semantic and world knowledge for coreference resolution. Can state-of-the-art coreference resolvers benefit from new kinds of features that encode semantic and world knowledge? Can such knowledge be induced from raw text, or can it be robustly extracted from large-scale knowledge bases? Do new methods need to be designed to represent such knowledge so that it can be profitably exploited by coreference resolvers?

Leveraging domain resources for domain-specific coreference resolution. What kind of domain resources can benefit domain-specific coreference resolution? Can domain knowledge be reliably learned from raw text? Can we design domain adaptation methods for coreference resolution so that resources for one domain can be profitably reused for another, possibly related, domain?

Training and operational speed of knowledge-rich coreference resolution systems. Can state-of-the-art coreference resolvers operate in an efficient manner given their increasing complexity with respect to system architecture and the knowledge sources they rely on? What learning algorithms need to be developed so that learning-based resolvers can be efficiently trained on coreference corpora that are much larger than existing ones (e.g., OntoNotes)?

We particularly welcome submissions that demonstrate how sophisticated knowledge can be used to improve coreference resolution for less-investigated coreference tasks (e.g., bridging anaphora resolution, event coreference resolution, resolution to abstract entities), as well as submissions that address the challenges involved in the development and application of knowledge-rich approaches to coreference resolution for less-investigated and/or low-resource languages (e.g., issues that could complicate the extraction, induction, and/or use of sophisticated knowledge for coreference resolution in a low-resource setting or in a specific language). For submissions that involve a standard coreference task (e.g., English identity coreference resolution), it is imperative that an empirical comparison be made against the state of the art, possibly with a systematic analysis of what types of errors made by state-of-the-art resolvers are being addressed.

Important Dates

October 24, 2016: First call for papers issued
January 15, 2017: Paper submission
April 15, 2017: First round of reviews completed
June 30, 2017: Submission of revised versions
September 15, 2017: Second round of reviews completed
November 15, 2017: Final version

Submission

Articles submitted to this special issue must adhere to the Journal Style Guidelines. Style Guide and LaTeX style files can be found here. We encourage authors to keep their submissions below 30 pages.

Manuscripts will be processed via the journal submission system. Please register as an author and select the article type "Special Issue: Knowledge-rich Coreference Resolution". Additional submission details will be announced closer to the deadline.

Guest Editors

Maciej Ogrodniczuk, Institute of Computer Science, Polish Academy of Sciences
Vincent Ng, The University of Texas at Dallas

Guest Editorial Board

Antonio Branco, University of Lisbon, Portugal
Chen Chen, The University of Texas at Dallas, USA
Dan Cristea, A. I. Cuza University of Iaşi, Romania
Pascal Denis, INRIA, France
Sobha Lalitha Devi, AU-KBC Research Center, Anna University of Chennai, India
Greg Durrett, The University of Texas at Austin, USA
Lars Hellan, Norwegian University of Science and Technology, Norway
Veronique Hoste, Ghent University, Belgium
Yufang Hou, IBM, Ireland
Ryu Iida, National Institute of Information and Communications Technology, Japan
Sandra Kübler, Indiana University, USA
Ekaterina Lapshinova-Koltunski, Universität des Saarlandes, Germany
Emmanuel Lassalle, Global Systematic Investors LLP, UK
Sebastian Martschat, Heidelberg University, Germany
Costanza Navaretta, University of Copenhagen, Denmark
Anna Nedoluzhko, Charles University in Prague, Czech Republic
Constantin Orasan, University of Wolverhampton, UK
Massimo Poesio, University of Essex, UK
Sameer Pradhan, cemantix.org and Boulder Learning Inc., USA
Agata Savary, François Rabelais University Tours, France
Manfred Stede, Universität Potsdam, Germany
Veselin Stoyanov, Facebook, USA
Olga Uryupina, University of Trento, Italy
Yannick Versley, Heidelberg University, Germany
Bonnie Webber, University of Edinburgh, UK
Nianwen Xue, Brandeis University, USA
Bishan Yang, Carnegie Mellon University, USA
Heike Zinsmeister, Universität Hamburg, Germany