Publications

2017

EMNLP, 2017

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ACL (short), 2017

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WWW, 2017

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TACL, 2017

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2016

EECS Department, University of California, Berkeley, 2016

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2015

EMNLP, 2015

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2013

EMNLP, 2013

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ACL (short), 2013

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The Astrophysical Journal Supplement Series, 2013

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2012

EMNLP, 2012

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ACL (short), 2012

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2011

CoNLL Shared Task, 2011

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2010

Physical Review Letters, 2010

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CoLing, 2010

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ACL, 2010

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2009

ALTA, 2009

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The University of Sydney, 2009

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Johns Hopkins University, 2009

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2008

ALTA, 2008

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The Journal of Physical Chemistry B, 2008

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Software

One-Endpoint Crossing Graph Parser

A range of tools related to one-endpoint crossing graphs - parsing, format conversion, and evaluation.

Coreference Error Analysis

A tool for classifying errors in coreference resolution.

CCG to PST

A tool for converting CCG derivations into PTB-style phrase structure trees.

Parse Error Analysis

A tool for classifying mistakes in the output of parsers.

Data

IE/NER from Cybercriminal Forums

Forum posts with annotations of products.

Crowdsourced Paraphrases

Paraphrases collected while conducting experiments on factors influencing crowd performance.

Spine and Arc version of the Penn Treebank

Code to convert the standard Penn Treebank into a version where each word is assigned a spine of non-terminals, and arcs to indicate attachments from one spine to another.

Adaptive CCG Supertagging Model

A model for the C&C supertagger that gives the same results with smaller beam sizes, enabling faster parsing.

Recent Posts

Papers I’m reading and more (RSS Feed)

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To apply attention across multiple input sources, it is best to apply attention independently and then have a second phase of attention over the summary vectors for each source.

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A neural transition based parser with actions to create non-local links can perform well on Minimal Recursion Semantics parsing.

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Breaking discourse parsing into separate relation identification and labeling tasks can boost performance (by dealing with limited training data).

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Parsing performance on the semantic structures of UCCA can be boosted by using a transition system that combines ideas from discontinuous and constituent transition systems, covering the full space of structures.

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Vectors for words and entities can be learned by trying to model the text written about the entities. This leads to word vectors that score well on similarity tasks and entity vectors that produce excellent results on entity linking and question answering.

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Contact

  • jkummerf@umich.edu
  • 2260 Hayward Street, Ann Arbor, MI 48109, USA