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Bilingual documentation has become a common phenomenon in many official institutions and private companies. In this scenario, the categorization of bilingual text is a useful tool, that can be also applied in the machine translation field. To tackle this classification task, different approaches will be proposed. On the one hand, two finite-state transducer algorithms from the grammatical inference domain will be discussed. On the other hand, the well-known naive Bayes approximation will be presented along with a possible modelization based on n-gram language models. Experiments carried out on a bilingual corpus have demonstrated the adequacy of these methods and the relevance of a second information source in text classification, as supported by classification error rates. Relative reduction of 29% with respect to the best previous results on the monolingual version of the same task has been obtained. [ABSTRACT FROM AUTHOR]

Statistical pattern recognition has proved to be an interesting framework for machine translation, and stochastic finite-state transducers are adequate models in many language processing areas such as speech translation, computer-assisted translations, etc. The well-known n-gram language models are widely used in this framework for machine translation. One of the application of these n-gram models is to infer stochastic finite-state transducers. However, only simple dependencies can be modelled, but many translations require to take into account strong context and style dependencies. Mixtures of parametric models allow to increase the description power of the statistical models by modelling subclasses of objects. In this work, we propose the use of n-gram mixtures in GIATI, a procedure to infer stochastic finite-state transducers. N-gram mixtures are expected to model topics or writing styles. We present experimental results showing that translation performance can be improved if enough training data is available. [ABSTRACT FROM AUTHOR]

One area of pattern recognition that is receiving a lot of attention recently is handwritten text recognition. Traditionally, handwritten text recognition systems have been modelled by means of HMM models and n-gram language models. The problem that n-grams present is that they are not able to capture long-term constraints of the sentences. Stochastic context-free grammars (SCFG) can be used to overcome this limitation by rescoring a n-best list generated with the HMM-based recognizer. Howerver, SCFG are known to have problems in the estimation of comlpex real tasks. In this work we propose the use of a combination of n-grams and category-based SCFG together with a word distribution into categories. The category-based approach is thought to simplify the SCFG inference process, while at the same time preserving the description power of the model. The results on the IAM-Database show that this combined scheme outperforms the classical scheme. [ABSTRACT FROM AUTHOR]

Spanglish is the simultaneous use, or alternating of both, traditional Spanish and English within the same conversational event. This interlanguage is commonly used in U.S. populations with large percentages of Spanish speakers. Despite the popularity of this dialect, and the wide spread of automated voice systems, currently there are no spoken dialog applications that can process Spanglish. In this paper we present the first attempt towards creating a Spanglish language model. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid language model which combines a word-based n-gram and a category-based Stochastic Context-Free Grammar (SCFG) is evaluated for training data sets of increasing size. Different estimation algorithms for learning SCFGs in General Format and in Chomsky Normal Form are considered. Experiments on the UPenn Treebank corpus are reported. These experiments have been carried out in terms of the test set perplexity and the word error rate in a speech recognition experiment. [ABSTRACT FROM AUTHOR]

The partial success in inferring the characteristics of offenders from their criminal behaviour (‘offender profiling') has relied on limited data and subjective judgments. We therefore sought to determine if Information Retrieval techniques and in particular Language Modelling could be applied directly to existing police digital records of criminal events to identify significant characteristics of offenders. The categories selected were gender and age group. Results showed that distinct differences in characteristics do exist. [ABSTRACT FROM AUTHOR]