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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)
Abstract: First story detection (FSD) involves identifying first stories about events from a continuous stream of documents. A major problem in this task is the high degree of lexical variation in documents which makes it very difficult to detect stories that talk about the same event but expressed using different words. We suggest using paraphrases to alleviate this problem, making this the first work to use paraphrases for FSD. We show a novel way of integrating paraphrases with locality sensitive hashing (LSH) in order to obtain an efficient FSD system that can scale to very large datasets. Our system achieves state-of-the-art results on the first story detection task, beating both the best supervised and unsupervised systems. To test our approach on large data, we construct a corpus of events for Twitter, consisting of 50 million documents, and show that paraphrasing is also beneficial in this domain. 1
URL: http://www.aclweb.org/anthology-new/N/N12/N12-1034.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.374.1750
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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)
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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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