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Workshop 1: Comparing journal apps and Keeping up to date Beskrivning: The aim of this workshop is an extensive update of the numerous possibilities for librarians and medical professionals to keep up to date about medical scientific publications and news. The latest developments of content aggregation and curation offer opportunities for librarians to not just keep up to date, but also to share and publish the acquired knowledge among colleagues and patrons. After this workshop, participants will be able to keep up to date more efficiently and have a broad knowledge of a wide range of tools for advising patrons. Supporting and facilitating medical professionals in education, patient-care and research is the main focus for medical and health librarians. We constantly have to attempt to make their workflow more efficient and easier. To be able to advise patrons about efficient ways of keeping up to date, we have to know ourselves what tools are out there. Because if we do not advise them, they will (have to) find out themselves, with a risk of missing out on relevant resources or possibilities. No one tool fits all. It is all about knowing in detail what a patron wants and match it with tools that can facilitate that requirement most efficiently. And the options for them to choose from are almost limitless either for free or via commercial parties. In this workshop we will go more into more detail on these possibilities. I will cover the scope of common practices and possibilities as completely as possible, showcased by various licensed and free web tools. Email alerts, Saved search, RSS and Readers, Table of Contents, Apps by Publishers and content providers will be discussed, but Journal Apps plus Web Media content aggregation and curation will take the main part of this course. Inför workshoppen: No prior knowledge required. Bringing your own mobile device(s) will enhance the hands-on learning effect. Workshopledare: Guus van den Brekel Tid & lokal: 8.30 - 12.00 Strix, von Eulers väg 4 b Antal deltagare: 30

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Language modeling plays a critical role in many natural language processing (NLP) tasks such as text prediction, machine translation and speech recognition. Traditional statistical language models (e.g. n-gram models) can only offer words that have been seen before and can not capture long word context. Neural language model provides a promising solution to surpass this shortcoming of statistical language model. This paper investigates Recurrent Neural Networks (RNNs) language model for Vietnamese, at character and syllable-levels. Experiments were conducted on a large dataset of 24M syllables, constructed from 1,500 movie subtitles. The experimental results show that our RNN-based language models yield reasonable performance on the movie subtitle dataset. Concretely, our models outperform n-gram language models in term of perplexity score.

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