1. Ergun Biçici. Enerji Harcamalarını Azaltmak için Bulut Monitorü (A Cloud Monitor for Reducing Energy Consumption). In First Symposium on Cloud Computing and Big Data (B3S17), Antalya, Turkey, pages 117-122, 10 2017. TÃœBITAK. [WWW]
   

   @InProceedings{Bicici:CloudMonitor:B3S2017,
   author = {Ergun Bi\c{c}ici},
   title = {Enerji Harcamalarını Azaltmak için Bulut Monitorü ("A Cloud Monitor for Reducing Energy Consumption")},
   booktitle = {{F}irst {S}ymposium on {C}loud {C}omputing and {B}ig {D}ata ({B3S17})},
   month = {10},
   year = {2017},
   address = {Antalya, Turkey},
   pages = {117--122},
   url = {http://www.b3s.b3lab.org/},
   publisher = {TÃœBITAK},
   abstract = {},
   
   }
   




2. Ergun Biçici. Predicting Translation Performance with Referential Translation Machines. In Proc. of the Second Conf. on Machine Translation (WMT17), Copenhagen, Denmark, pages 540-544, 9 2017. [WWW] Keyword(s): Machine Translation, Machine Learning, Performance Prediction. Abstract:

   Referential translation machines achieve top performance in both bilingual and monolingual settings without accessing any task or domain specific information or resource. RTMs achieve the $3$rd system results for German to English sentence-level prediction of translation quality and the $2$nd system results according to root mean squared error. In addition to the new features about substring distances, punctuation tokens, character $n$-grams, and alignment crossings, and additional learning models, we average prediction scores from different models using weights based on their training performance for improved results.

   
   

   @InProceedings{Bicici:RTM:WMT2017,
   author = {Ergun Bi\c{c}ici},
   title = {Predicting Translation Performance with Referential Translation Machines},
   booktitle = {Proc. of the {S}econd {C}onf. on {M}achine {T}ranslation ({WMT17})},
   month = {9},
   year = {2017},
   address = {Copenhagen, Denmark},
   pages = {540--544},
   url = {http://www.aclweb.org/anthology/W17-4759},
   keywords = "Machine Translation, Machine Learning, Performance Prediction",
   abstract = {Referential translation machines achieve top performance in both bilingual and monolingual settings without accessing any task or domain specific information or resource. RTMs achieve the $3$rd system results for German to English sentence-level prediction of translation quality and the $2$nd system results according to root mean squared error. In addition to the new features about substring distances, punctuation tokens, character $n$-grams, and alignment crossings, and additional learning models, we average prediction scores from different models using weights based on their training performance for improved results.},
   
   }
   




3. Ergun Biçici. RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity. In 11th International Workshop on Semantic Evaluation (SemEval-2017), Vancouver, Canada, pages 194-198, 8 2017. [PDF] Abstract:

   We use referential translation machines for predicting the semantic similarity of text in all STS tasks which contain Arabic, English, Spanish, and Turkish this year. RTMs pioneer a language independent approach to semantic similarity and remove the need to access any task or domain specific information or resource. RTMs become 6th out of 52 submissions in Spanish to English STS. We average prediction scores using weights based on the training performance to improve the overall performance.

   
   

   @InProceedings{Bicici:RTM:SEMEVAL2017,
   author = {Ergun Bi\c{c}ici},
   title = {{RTM} at {SemEval-2017} Task 1: Referential Translation Machines for Predicting Semantic Similarity},
   booktitle = {11th International Workshop on Semantic Evaluation ({SemEval-2017})},
   month = {8},
   year = {2017},
   address = {Vancouver, Canada},
   pages = {194--198},
   abstract = {We use referential translation machines for predicting the semantic similarity of text in all STS tasks which contain Arabic, English, Spanish, and Turkish this year. RTMs pioneer a language independent approach to semantic similarity and remove the need to access any task or domain specific information or resource. RTMs become 6th out of 52 submissions in Spanish to English STS. We average prediction scores using weights based on the training performance to improve the overall performance.},
   url = {http://nlp.arizona.edu/SemEval-2017/pdf/SemEval030.pdf} 
   }
   

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