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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Annotator sequence bias, where the label for one item affects the label for the next, occurs across a range of datasets. Avoid it by separately randomise the order of items for each annotator.

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The simplest way to learn word vectors for rare words is to average their context. Tweaking word2vec to make greater use of the context may do slightly better, but it’s unclear.

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It seems intuitive that a coreference system could benefit from information about what nouns a verb selects for, but experiments on explicitly adding a representation of it to a neural system does not lead to gains, implying it is already learning them or they are not useful.

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Training a single parser on multiple domains can improve performance, and sharing more parameters (encoder and decoder as opposed to just one) seems to help more.

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To explain structured outputs in terms of which inputs have most impact, treat it as identifying components in a bipartite graph where weights are determined by perturbing the input and observing the impact on outputs.

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Contact

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