Tuning statistical machine translation parameters using perplexity

Nabhan, Ahmed Ragab; Rafea, Ahmed

doi:10.1109/iri-05.2005.1506496

Cited by 11 publications

(2 citation statements)

References 4 publications

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“…It may not classify exactly since there can be missing data and overfitting problems (Witten and Frank, 2000;Duda et al, 2001). Overfitting means "fitting too much the training data and the model starts to degrade performance on test data" (Nabhan and Rafea, 2005). The problem occurs when noisy or erroneous data are added to a data set (refer to Figure 2).…”

Section: Classification Methods In Data Miningmentioning

confidence: 99%

Detection and prediction of alternative splicing in Arabidopsis thaliana

Park

Falcone

Daniels

2008

IJCBDD

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Alternative splicing is an important process for increasing the diversity arising from a single gene. Presently, most studies aimed at detecting alternatively spliced genes use Expressed Sequence Tags (ESTs). However, the EST studies based on spliced transcripts analyse sequences by alignment rather than sequence patterns. Second, EST libraries can be of uncertain quality. To address these issues and to improve the quality of detection and prediction for alternative splicing, we propose a method that primarily uses pre-mRNAs. It is achieved by a decision tree algorithm using triplet nucleotides as attributes for each chromosome in Arabidopsis thaliana. In addition, we propose a novel algorithm for accurate prediction.

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Section: Classification Methods In Data Miningmentioning

confidence: 99%

Detection and prediction of alternative splicing in Arabidopsis thaliana

Park

Falcone

Daniels

2008

IJCBDD

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“…MERT is a method that attempt to optimize the parameter of the model while considering a more complex evaluation than simply counting incorrect translation and attempt to train the model based on the method that will be used to evaluate the model [18]. The tuning process is done to develop better translation model than the one created on the training part [19]. The goal of MERT is to find a minimum error rate count on a representative corpus 𝑓 1 𝑆 with given translations 𝑒 ˆ1𝑆 and a set of K different candidate translations 𝐂 𝑠 = {𝐞 𝑠,1 , … , 𝐞 𝑠,𝐾 }.…”

Section: Tuningmentioning

confidence: 99%

An empirical evaluation of phrase-based statistical machine translation for Indonesia slang-word translator

Eleison¹,

Hutahaean²,

Tampubolon³

et al. 2022

IJEECS

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The use of slang (non-standard language), especially in social media, is increasing. It causes reducing the level of understanding when communicating because not everyone understands slang (non-standard language). The purpose of this work is to develop a slang-word translator. The other objective is to find the minimum number of sentences and BiLingual Evaluation Understudy (BLEU) score used as a benchmark to determine that the translation is understandable. The approach used in this project is a Phrase-based statistical machine translation (PBSMT) approach, suitable for low resource language, with a dataset of 100,000 sentences taken from the comments column of several online political news portals. The comments are then manually translated to produce a parallel corpus of non-standard language-standard language. The sample sentences are taken from the dataset then distributed using questionnaires to obtain the human understanding level regarding the translation result. The result of the implementation is a BLEU score of 64 and the minimum number of sentences to have an understandable machine translation is 500. The conclusion drawn from the distributed questionnaires is that humans can understand the sentences produced by the translation machine.

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How evolutionary algorithms are applied to statistical natural language processing

Araujo

2007

Artif Intell Rev

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Statistical natural language processing (NLP) and evolutionary algorithms (EAs) are two very active areas of research which have been combined many times. In general, statistical models applied to deal with NLP tasks require designing specific algorithms to be trained and applied to process new texts. The development of such algorithms may be hard. This makes EAs attractive since they offer a general design, yet providing a high performance in particular conditions of application. In this article, we present a survey of many works which apply EAs to different NLP problems, including syntactic and semantic analysis, grammar induction, summaries and text generation, document clustering and machine translation. This review finishes extracting conclusions about which are the best suited problems or particular aspects within those problems to be solved with an evolutionary algorithm.

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Tuning statistical machine translation parameters using perplexity

Cited by 11 publications

References 4 publications

Detection and prediction of alternative splicing in Arabidopsis thaliana

Detection and prediction of alternative splicing in Arabidopsis thaliana

An empirical evaluation of phrase-based statistical machine translation for Indonesia slang-word translator

How evolutionary algorithms are applied to statistical natural language processing

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