Titlebook: Computational Linguistics and Intelligent Text Processing; 15th International C Alexander Gelbukh Conference proceedings 2014 Springer-Verl

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https://doi.org/10.1007/978-981-97-0558-0g the presented approach to generalize across dialects with some minor modifications.   Results of experiments, that targeted Egyptian Arabic, show the approach’s ability to detect subjective internet slang represented by single words or by multi-word expressions, as well as classifying the polarity

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Luke K. Fryer,Ronnel B. King,Lily M. Zeng, Apache Tika and Poppler in various configurations. From the output of these tools we recover proper boundaries using on-the-fly language models and language-independent extraction heuristics. In our research, we looked especially at publications from the European Union, which constitute a valuable

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https://doi.org/10.1007/978-3-319-05567-1elop many models of CRF using these feature combinations. In order to further improve the performance the outputs of these classifiers are combined together using a classifier ensemble technique based on modified DE. Our experiments with the benchmark datasets yield the recall, precision and F-measu

GUILT

Verb Clustering for Brazilian Portuguese those developed for English without language-specific feature engineering. This yields interesting results which line up well with those obtained for other languages, demonstrating the cross-linguistic nature of this type of classification. However, we also discover and discuss issues which require

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Grating

Compound Terms and Their Multi-word Variants: Case of German and Russian Languages variation patterns for these two languages and demonstrate that the transformation of a morphological compound into a syntagmatic compound mainly occurs when the term denomination needs to be enlarged.

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