TextGraphs-11: Graph-based Methods for Natural Language Processing
Workshop at ACL 2017, Vancouver, Canada, August 2017

TextGraphs-11 is proudly sponsored by Verisk Analytics!


Submission deadline extended

Due to multiple requests, we have decided to extend the submission deadline for the TextGraphs workshop. The news deadline is April 30th, anywhere in the world (AoE, UTC-12).  

Multiple-submission policy

Papers that have been or will be submitted to other meetings or publications must indicate this at submission time. Authors submitting multiple papers to TextGraphs-11 may not submit papers that overlap significantly (>50%) with each other in content or results. Authors of papers accepted for presentation at Textgraphs-11 must notify the organizers immediately as to whether the paper will be presented. All accepted papers must be presented at the conference to appear in the proceedings.


The workshops in the TextGraphs series have published and promoted the synergy between the field of Graph Theory and Natural Language Processing. Besides traditional NLP applications like word sense disambiguation and semantic role labeling, and information extraction graph-based solutions nowadays also target new web-scale applications  like information propagation in social networks, rumor proliferation, e-reputation, language dynamics learning, and future events prediction, to name a few. 

       >> Previous editions of the TextGraphs workshop

The eleventh edition of the TextGraphs workshop aims to extend the focus on issues and solutions for large-scale graphs, such as those derived for web-scale knowledge acquisition or social networks. We encourage the description of novel NLP problems or applications that have emerged in recent years, which can be addressed with graph-based methods. Furthermore, we also encourage research on  applications of graph-based methods in the area of Semantic Web in order to link them to related NLP problems and applications.


In an exciting and completely novel extension, we encourage graph-based interpretations of deep learning models for NLP tasks. Though deep learning models are displaying state of the art performance on many NLP tasks, they are often criticized for not being interpretable. In TextGraphs-11 we introduce a new challenge for graph-based methods: reasoning and interpretation of the layers used in deep learning models. Given that a neural network is, from one point of view, a graph through which activation scores are propagated, many of the existing graph-based methods used in our workshop community could potentially be  directly applicable. Can graph-based methods be harnessed to provide information to make deep processing comprehensible for humans and computers? What are the capabilities and limits when graph-based methods are applied to neural networks in general? Which aspects of the networks are not susceptible to such treatment and why not?dedede


TextGraphs-11 invites submissions on (but not limited to) the following topics:

  • Graph-based methods for providing reasoning and interpretation of deep learning methods
    • Graph-based methods for reasoning and interpreting deep processing by neural networks,
    • Explorations of the capabilities and limits of graph-based methods applied to neural networks in general
    • Investigation of which aspects of neural networks are not susceptible to graph-based methods.    
  • Graph-based methods for Information Retrieval, Information Extraction, and Text Mining
    • Graph-based methods for word sense disambiguation,
    • Graph-based representations for ontology learning,
    • Graph-based strategies for semantic relations identification,
    • Encoding semantic distances in graphs,
    • Graph-based techniques for text summarization, simplification, and paraphrasing
    • Graph-based techniques for document navigation and visualization
    • Reranking with graphs
    • Applications of label propagation algorithms, etc.
  • New graph-based methods for NLP applications
    • Random walk methods in graphs
    • Spectral graph clustering
    • Semi-supervised graph-based methods
    • Methods and analyses for statistical networks
    • Small world graphs
    • Dynamic graph representations
    • Topological and pretopological analysis of graphs
    • Graph kernels, etc.
  • Graph-based methods for applications on social networks
    • Rumor proliferation
    • E-reputation
    • Multiple identity detection
    • Language dynamics studies
    • Surveillance systems, etc.
  • Graph-based methods for NLP and Semantic Web
    • Representation learning methods for knowledge graphs (i.e., knowledge graph embedding)
    • Using graphs-based methods to populate ontologies using textual data,
    • Inducing knowledge of ontologies into NLP applications using graphs,
    • Merging ontologies with graph-based methods using NLP techniques.


TextGraphs-11 workshop fully embraces the following ACL's anti-harassment policy. 

The open exchange of ideas, the freedom of thought and expression, and respectful scientific debate are central to the aims and goals of the ACL. These require a community and an environment that recognizes the inherent worth of every person and group, that fosters dignity, understanding, and mutual respect, and that embraces diversity. For these reasons, ACL is dedicated to providing a harassment-free experience for all the members, as well as participants at our events and in our programs.

Harassment and hostile behavior are unwelcome at any ACL conference, associated event, or in ACL-affiliated on-line discussions. This includes: speech or behavior that intimidates, creates discomfort, or interferes with a person's participation or opportunity for participation in a conference or an event. We aim for ACL-related activities to be an environment where harassment in any form does not happen, including but not limited to: harassment based on race, gender, religion, age, color, appearance, national origin, ancestry, disability, sexual orientation, or gender identity. Harassment includes degrading verbal comments, deliberate intimidation, stalking, harassing photography or recording, inappropriate physical contact, and unwelcome sexual attention. The policy is not intended to inhibit challenging scientific debate, but rather to promote it through ensuring that all are welcome to participate in shared spirit of scientific inquiry.

It is the responsibility of the community as a whole to promote an inclusive and positive environment for our scholarly activities. In addition, anyone who experiences harassment or hostile behavior may contact any current member of the ACL Executive Committee or contact Priscilla Rasmussen (acl@aclweb.org), who is usually available at the registration desk during ACL conferences. Members of the executive committee will be instructed to keep any such contact in strict confidence, and those who approach the committee will be consulted before any actions are taken.

Community members with harassment concerns should contact the organizers of the workshop. In case of a formal complaint, the contacted person will first speak to all parties involved to try to resolve the issue without presupposition of guilt. 


We thank Verisk Analytics for the sponsorship of TextGraphs-11! 


Verisk Analytics is a leading data analytics provider serving customers in insurance, natural resources, financial services, government, and risk management. Verisk Analytics is a member of Standard & Poor’s S&P 500® Index and part of the Nasdaq-100 Index. In 2016, Forbes named Verisk to its World’s Most Innovative Companies list and to its America’s Best Large Employer list. Verisk is one of only 14 companies to appear on both lists. Verisk is headquartered in Jersey City, New Jersey, right across the Hudson River from downtown Manhattan.