Workshop on Machine Reading
Co-located with K-Cap 2017
December 4, 2017
Machine Reading holds significant potential for automating knowledge capture, especially given the continuing improvements in natural-language processing technologies. Macro-reading techniques (skimming many documents) now enable collecting large databases of facts, while modern micro-reading techniques (comprehension of individual paragraphs) have proven effective at factoid question answering. In this workshop, participants will discuss ways to develop new capabilities in macro- and micro-reading to take these to the next level, in particular to extract useful representations of text (be they symbolic, neural, or a hybrid) that enable, for example, automated reasoning to answer non-trivial questions.
Topics
Machine Reading is very broad, encompassing many subdiscipines of AI, and its potential to help with knowledge capture is largely undeveloped. Here is a sample of the topics relevant to the workshop:
- advances and new directions in NLP
- methods of active learning for guiding machine readers to useful content
- methods for (dis)confirming content derived from text
- extracting content from tables and diagrams
- integrating extracted information into a knowledge base
- hybrids methods that combine "deep NLP" and symbolic logic
- ways that macro reading might inform micro reading, and vice versa
However, participants are encouraged to add to this list and to bring their own perspectives to the workshop.
Participation
The workshop will be organized as a working session with the aim of fostering discussion and sharing big ideas, even if they are untested. In this way, the workshop is not a traditional conference organized around the presentation of papers. If you would like to participate, please submit a short (1-2 page) position paper that describes your experience with machine reading research, if any, and your interests and plans for pursuing it. Submit your position paper by sending a PDF document to porter [at] cs.utexas.edu.
Schedule
- Deadline to submit your position paper: September 24, 2017
- Response on whether your request to participate can be accommodated: October 1st, 2017
- Workshop: December 4, 2017
Organizers
- Bruce Porter, University of Texas at Austin, porter [at] cs.utexas.edu
- Peter Clark, Allen Institute for Artificial Intelligence, peterc [at] allenai.org
- Ken Barker, IBM's Thomas J. Watson Research Center, kjbarker [at] us.ibm.com