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Translingual text mining for identification of language pair phenomena
2016
Translingual Text Mining (TTM) is an innovative technology of natural language processing for building multilingual parallel corpora, processing machine translation, contextual knowledge acquisition, information extraction, query profiling, language modeling, contextual word sensing, creating feature test sets and for variety of other purposes. The Keynote Lecture will discuss opportunities and challenges of this computational technology. In particular, the focus will be made on identification of language pair phenomena and their applications to building holistic language model which is a novel tool for processing machine translation, supporting professional translations, evaluation of tran…
Extraction of Medical Terms for Word Sense Disambiguation within Multilingual Framework
2016
All the languages belonging to the same language family have a certain number of the common characteristics called language pair phenomena, which can be found quite useful for processing them for multilingual purposes like translation across the cognate languages, building dictionaries, thesauri, transcript collections, or for multilingual text retrieval of digital documents. In addition, it is estimated that more than 30% of English vocabulary has been inherited from Latin, which has dominated medical terminology in particular. We use this fact by exploring word sense disambiguation (WSD) in multilingual environment. Specifically in the medical domain, language pair phenomena can be limite…
Genetic Normalized Convolution
2011
Normalized convolution techniques operate on very few samples of a given digital signal and add missing information, trough spatial interpolation. From a practical viewpoint, they make use of data really available and approximate the assumed values of the missing information. The quality of the final result is generally better than that obtained by traditional filling methods as, for example, bilinear or bicubic interpolations. Usually, the position of the samples is assumed to be random and due to transmission errors of the signal. Vice versa, we want to apply normalized convolution to compress data. In this case, we need to arrange a higher density of samples in proximity of zones which c…