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1
Parsing time: Learning to interpret time expressions
In: http://www.aclweb.org/anthology-new/N/N12/N12-1049.pdf (2012)
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2
Three dependency-and-boundary models for grammar induction
In: http://www-nlp.stanford.edu/pubs/dbm.pdf (2012)
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3
Three dependency-and-boundary models for grammar induction
In: http://www.stanford.edu/%7Ejurafsky/dbm.pdf (2012)
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4
Three dependency-and-boundary models for grammar induction
In: http://www.aclweb.org/anthology-new/D/D12/D12-1063.pdf (2012)
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5
Punctuation: Making a point in unsupervised dependency parsing
In: http://www.stanford.edu/%7Evals/pubs/punctuation.pdf (2011)
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6
The Best Lexical Metric for Phrase-Based Statistical MT System Optimization
In: http://www.mt-archive.info/NAACL-HLT-2010-Cer-1.pdf (2010)
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7
Phrasal: a toolkit for statistical machine translation with facilities for extraction and incorporation of arbitrary model features.
In: https://www.microsoft.com/en-us/research/wp-content/uploads/2010/06/Phrasal-A-Statistical-Machine-Translation-Toolkit-for-Exploring-New-Model-Features.pdf (2010)
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8
Baby Steps: How “Less is More” in unsupervised dependency parsing
In: http://www0.cs.ucl.ac.uk/staff/rmartin/grll09/spikovsky1.pdf (2009)
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9
Baby Steps: How “Less is More” in unsupervised dependency parsing
In: http://nlp.stanford.edu/pubs/babysteps.pdf (2009)
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10
Cheap and fast - but is it good? evaluating nonexpert annotations for natural language tasks
In: http://sing.stanford.edu/cs303-sp11/papers/snow_turk.pdf (2008)
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11
Cheap and fast — but is it good? Evaluating non-expert annotations for natural language tasks
In: http://blog.doloreslabs.com/wp-content/uploads/2008/09/amt_emnlp08_final.pdf (2008)
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12
Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks. EMNLP’08
In: http://www.aclweb.org/anthology-new/D/D08/D08-1027.pdf (2008)
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13
Cheap and fast — but is it good? Evaluating non-expert annotations for natural language tasks
In: http://nlp.stanford.edu/pubs/amt_emnlp08.pdf (2008)
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14
Cheap and fast — But is it good? Evaluating non-expert annotations for natural language tasks
In: http://blog.doloreslabs.com/wp-content/uploads/2008/09/amt_emnlp08_accepted.pdf (2008)
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15
The detection of emphatic words using acoustic and lexical features
In: http://www.stanford.edu/~jurafsky/p1034.pdf (2005)
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16
A Conditional Random Field Word Segmenter for Sighan Bakeoff 2005
In: http://wing.comp.nus.edu.sg/~antho/I/I05/I05-3027.pdf (2005)
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17
Automatic tagging of Arabic text: from raw text to base phrase chunks
In: http://www.stanford.edu/~jurafsky/ArabicChunk.pdf (2004)
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18
A.: Learning syntactic patterns for automatic hypernym discovery.
In: http://papers.nips.cc/paper/2659-learning-syntactic-patterns-for-automatic-hypernym-discovery.pdf (2004)
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19
Effects of disfluencies, predictability, and utterance position on word form variation in English conversation
In: http://www.colorado.edu/ling/jurafsky/jasa02.pdf (2003)
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20
A Bayesian model predicts human parse preference and reading time in sentence processing
In: http://books.nips.cc/papers/files/nips14/CS14.pdf (2002)
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