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| This workshop addresses the challenges of learning in an unsupervised or minimally supervised context with questions of linguistic structure. Inducing structured linguistic representations from text has long been a fundamental problem in Computational Linguistics and Natural Language Processing, drawing from theoretical Computer Science and Machine Learning. The popularity of the area is driven by two different motivations. Firstly, it can help us to better understand the cognitive process of language acquisition in humans. Secondly, it can help with portability of NLP applications into new domains and new languages. Most NLP algorithms rely on syntactic parse structure created by supervised methods, however in many cases there is no available training data, thus limiting the portability of these algorithms. Consequently work on unsupervised induction of the linguistic structure of language holds considerable promise, although current approaches are a long way from solving the general problems. This workshop aims to foster continuing research in structure induction, and bring together different communities working on these problems, be it from a cognitive or a text processing processing perspective. | This workshop addresses the challenges of learning in an unsupervised or minimally supervised context with questions of linguistic structure. Inducing structured linguistic representations from text has long been a fundamental problem in Computational Linguistics and Natural Language Processing, drawing from theoretical Computer Science and Machine Learning. The popularity of the area is driven by two different motivations. Firstly, it can help us to better understand the cognitive process of language acquisition in humans. Secondly, it can help with portability of NLP applications into new domains and new languages. Most NLP algorithms rely on syntactic parse structure created by supervised methods, however in many cases there is no available training data, thus limiting the portability of these algorithms. Consequently work on unsupervised induction of the linguistic structure of language holds considerable promise, although current approaches are a long way from solving the general problems. This workshop aims to foster continuing research in structure induction, and bring together different communities working on these problems, be it from a cognitive or a text processing perspective. |
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| João Graça, L2F INESC ID | |
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| João Graça, Spoken Language Systems Lab, INESC-ID Lisboa ---------------------------- |
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Percy Liang - Stanford University |
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FIRST CALL FOR PAPERS AND SHARED TASK PARTICIPATION
The Workshop on Induction of Linguistic Structure (WILS)
Co-located with NAACL-HLT 2012 Montreal, Quebec, Canada; June 07, 2012
http://wiki.cs.ox.ac.uk/InducingLinguisticStructure
Submission Deadline: March 26, 2012
Workshop description
This workshop addresses the challenges of learning in an unsupervised or minimally supervised context with questions of linguistic structure. Inducing structured linguistic representations from text has long been a fundamental problem in Computational Linguistics and Natural Language Processing, drawing from theoretical Computer Science and Machine Learning. The popularity of the area is driven by two different motivations. Firstly, it can help us to better understand the cognitive process of language acquisition in humans. Secondly, it can help with portability of NLP applications into new domains and new languages. Most NLP algorithms rely on syntactic parse structure created by supervised methods, however in many cases there is no available training data, thus limiting the portability of these algorithms. Consequently work on unsupervised induction of the linguistic structure of language holds considerable promise, although current approaches are a long way from solving the general problems. This workshop aims to foster continuing research in structure induction, and bring together different communities working on these problems, be it from a cognitive or a text processing perspective.
In this workshop, we solicit papers from many subfields of computational linguistics and language processing. Topics include, but are not limited to
- grammar learning
- part-of-speech and shallow syntax
- learning semantic representations
- inducing document and discourse structure
- learning/projecting structures across multilingual corpora
- relation induction across document collections
- evaluation of induced representations
Our aim is to bring together work on fully unsupervised methods along with minimally supervised approaches (e.g., domain adaptation and multilingual projection).
The workshop will solicit short papers (6 pages) for either oral or poster presentation. More details on paper submission will be provided in due course on the workshop website.
The workshop will host the PASCAL Unsupervised grammar induction challenge, which aims to foster continuing research in grammar induction and part-of-speech induction, while also opening up the problem to more ambitious settings, including a wider variety of languages, removing the reliance on gold standard parts-of-speech and, critically, providing a thorough evaluation including a task-based evaluation.
The shared task will evaluate dependency grammar induction algorithms, evaluating the quality of structures induced from natural language text. In contrast with the defacto standard experimental setup, which starts with gold standard part-of-speech tags, we will encourage competitors to submit systems which are completely unsupervised. The evaluation will consider the standard dependency tree based measures as well as measures over the predicted parts of speech. Our aim is to allow a wide range of different approaches, and for this reason we will accept submissions which predict just the dependency trees for gold PoS, just the PoS, or both jointly.
While our focus is on unsupervised approaches, we recognise that there has been considerable related research using semi-supervised learning, domain adaption, cross-lingual projection and other partially supervised methods for building syntactic models. For this reason we will also support these kinds of systems.
Important dates
Submission Deadline: March 26
Notification of Acceptance: April 23
Camera-ready papers Due: May 04
Workshop: June 07, 2012
Shared task dates
Data made available: Jan 27
Submissions due for evaluation: April 13
Evaluation results released: April 23
Team reports due: May 4
Organizers
Trevor Cohn, University of Sheffield
Phil Blunsom, University of Oxford
João Graça, Spoken Language Systems Lab, INESC-ID Lisboa
Program committee
Ben Taskar - University of Pennsylvania
Percy Liang - Stanford University
Andreas Vlachos - University of Cambridge
Chris Dyer - CMU
Mark Drezde - John Hopkins
Shai Cohen - Columbia University
Kuzman Ganchev - Google Inc.
André Martins - CMU/IST Portugal
Greg Druck - Yahoo
Ryan McDonald - Google Inc.
Nathan Schneider - CMU
Partha Talukdar - CMU
Dipanjan Das - CMU
Mark Steedman - University of Edinburgh
Luke Zettlemoyer - University of Washington
Roi Reichart - MIT
David Smith - University of Massachusetts
Ivan Titov - Saarland University
Alex Clarke - Royal Holloway University
Khalil Sima'an - University of Amsterdam
Stella Frank - University of Edinburgh