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1
Using paraphrases for improving first story detection in news and twitter
In: http://homepages.inf.ed.ac.uk/miles/papers/naacl12.pdf (2012)
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2
Opinion retrieval in twitter
In: http://homepages.inf.ed.ac.uk/miles/papers/icwsm12.pdf (2012)
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3
Using paraphrases for improving first story detection in news and twitter
In: http://www.aclweb.org/anthology-new/N/N12/N12-1034.pdf (2012)
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4
Constructing parallel corpora for six indian languages via crowdsourcing
In: http://www.aclweb.org/anthology/W12-3152/ (2012)
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5
LRscore for evaluating lexical and reordering quality
In: http://aclweb.org/anthology-new/W/W10/W10-1749.pdf (2010)
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6
LRscore for evaluating lexical and reordering quality
In: http://www.mt-archive.info/WMT-2010-Birch.pdf (2010)
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7
A Gibbs sampler for phrasal synchronous grammar induction
In: http://nlp.csie.ncnu.edu.tw/~shin/acl-ijcnlp2009/proceedings/CDROM/ACLIJCNLP/pdf/ACLIJCNLP088.pdf (2009)
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8
Bayesian synchronous grammar induction
In: http://books.nips.cc/papers/files/nips21/NIPS2008_0238.pdf (2008)
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9
Modelling lexical redundancy for machine translation
In: http://www.mt-archive.info/Coling-ACL-2006-Talbot.pdf (2006)
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10
Modelling lexical redundancy for machine translation
In: http://acl.ldc.upenn.edu/P/P06/P06-1122.pdf (2006)
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11
Modelling lexical redundancy for machine translation
In: http://www.iccs.informatics.ed.ac.uk/~osborne/papers/acl06.pdf (2006)
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12
Constraining the phrase-based, joint probability statistical translation model
In: http://www.mt-archive.info/HLT-NAACL-2006-Birch.pdf (2006)
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13
Constraining the Phrase-Based, Joint Probability Statistical Translation Model
In: http://www.statmt.org/wmt06/proceedings/pdf/WMT23.pdf (2006)
Abstract: The joint probability model proposed by Marcu and Wong (2002) provides a strong probabilistic framework for phrase-based statistical machine translation (SMT). The model's usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present the first model to use word alignments for constraining the space of phrasal alignments searched during Expectation Maximization (EM) training. Constraining the joint model improves performance, showing results that are very close to state-of-the-art phrase-based models. It also allows it to scale up to larger corpora and therefore be more widely applicable.
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.5119
http://www.statmt.org/wmt06/proceedings/pdf/WMT23.pdf
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14
1 Statistical Natural Language Processing
In: http://www-csli.stanford.edu/~ccb/publications/statistical-natural-language-processing-chapter.pdf (2003)
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15
Statistical Natural Language Processing
In: http://www.cogsci.ed.ac.uk/~osborne/csli.pdf (2003)
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16
Using Language Models to Assist in the Correction of Machine Translation Output
In: http://www.ling.ed.ac.uk/teaching/postgrad/mscslp/archive/dissertations/2001-2/beatrice_alex.pdf (2002)
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17
Shallow Parsing with PoS Taggers and Linguistic Features
In: http://www.ai.mit.edu/projects/jmlr/papers/volume2/megyesi02a/megyesi02a.pdf (2002)
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18
Memory-Based Shallow Parsing
In: http://cnts.uia.ac.be/cnts/papers/./ps/20020417.4146.jmlr2002.ps (2002)
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19
Memory-based Shallow Parsing
In: http://jmlr.csail.mit.edu/papers/volume2/tks02a/tks02a.pdf (2002)
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20
Using Language Models to Assist in the Correction Of Machine Translation Output
In: http://www.cogsci.ed.ac.uk/~osborne/msc-projects/alex.ps.gz (2002)
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