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1
Efficient techniques for streaming cross document coreference resolution
Shrimpton, Luke William. - : The University of Edinburgh, 2017
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2
Learning to hash for large scale image retrieval
Moran, Sean James. - : The University of Edinburgh, 2016
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3
Real-time detection, tracking, and monitoring of automatically discovered events in social media
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4
Real-Time Detection, Tracking, and Monitoring of Automatically Discovered Events in Social Media
von Lunen, Alexander; McCreadie, Richard; Ciravegna, Fabio. - : Association for Computational Linguistics, 2014
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5
Introducing corpus-based rules and algorithms in a rule-based machine translation system
Dugast, Loic. - : The University of Edinburgh, 2013
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6
Real-time event detection in massive streams
Petrovic, Sasa. - : The University of Edinburgh, 2013
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7
Opinion Retrieval in Twitter
In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 6 No. 1 (2012): Sixth International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2012)
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8
Improving statistical machine translation with linguistic information
Hoang, Hieu. - : The University of Edinburgh, 2011
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9
Reordering metrics for statistical machine translation
Birch, Alexandra. - : The University of Edinburgh, 2011
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10
Stream-based statistical machine translation
Levenberg, Abby D.. - : The University of Edinburgh, 2011
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11
Cyril Goutte, Nicola Cancedda, Marc Dymetman, George Foster (eds.): Learning Machine Translation [Rezension]
In: Natural language engineering. - Cambridge : Cambridge University Press 16 (2010) 1, 99-100
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12
Metrics for MT evaluation: evaluating reordering
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 24 (2010) 1, 15-26
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13
Machine Translation for Twitter
Jehl, Laura Elisabeth. - : The University of Edinburgh, 2010
Abstract: We carried out a study in which we explored the feasibility of machine translation for Twitter for the language pair English and German. As a first step we created a small bilingual corpus of 1,000 tweets. Using this corpus we carried out an analysis of the linguistic features of tweets. We tested di erent strategies of domain adaptation and found that they improved translation performance. In our experiments we found large di erences in performance due to the handling of unknown words. By using xml-markup we were able to reduce this di erence. We also replaced special Twitter expressions with placeholders, which enabled us to learn more robust n-gram statistics from Twitter data. We carried out a small-scale human evaluation to balance our automatic scores. Finally, we tested strategies to enforce translation output of legal length. Generating n-best-lists of translation candidates and searching for legal tweets was found to be helpful, but ultimately too unreliable because there was no systematic way to determine the required value of n. We suggested a feature function based on character count as a potential solution.
Keyword: machine translation
URL: http://hdl.handle.net/1842/5317
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14
Automatic recognition of multiparty human interactions using dynamic Bayesian networks
Dielmann, Alfred. - : The University of Edinburgh, 2009
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15
Wide-coverage parsing for Turkish
Çakici, Ruket. - : The University of Edinburgh, 2009
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16
Active learning and logarithmic opinion pools for HPSG parse selection
In: Natural language engineering. - Cambridge : Cambridge University Press 14 (2008) 2, 191-222
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OLC Linguistik
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17
Diversity in logarithmic opinion pools
In: Lingvisticae investigationes. - Amsterdam : Benjamins 30 (2007) 1, 27-47
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18
Re-evaluating the role of BLEU in machine translation research
In: Association for Computational Linguistics / European Chapter. Conference of the European Chapter of the Association for Computational Linguistics. - Menlo Park, Calif. : ACL 11 (2006), 249-256
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19
Logarithmic Opinion Pools for Conditional Random Fields
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20
Scaling conditional random fields using error correcting codes
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