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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)
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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)
Abstract: Introduction Statistical natural language processing (SNLP) is a field lying in the intersection of natural language processing and machine learning. SNLP di#ers from traditional natural language processing in that instead of having a linguist manually construct some model of a given linguistic phenomenon, that model is instead (semi-) automatically constructed from linguistically annotated resources. Methods for assigning partof -speech tags to words, categories to texts, parse trees to sentences, and so on, are (semi-) automatically acquired using machine learning techniques. The recent trend of applying statistical techniques to natural language processing came largely from industrial speech recognition research groups at AT&T's Bell Laboratories and IBM's T.J. Watson Research Center. Statistical techniques in speech recognition have so vastly outstripped the performance of their non-statistical counterparts that rule-based speech recognition systems are essentially no longer a
URL: http://www.cogsci.ed.ac.uk/~osborne/csli.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.6279
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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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