This page compiles all the information regarding Finland.
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|Niklas Laxström||University of Helsinki||https://tuhat.halvi.helsinki.fi/portal/fi/persons/niklas-laxstroem%2894ff124b-209c-4be3-b67d-0a39868289a6%29.html|
This is a list of publications by authors of this country.
|Title||Author(s)||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Situated Interaction in a Multilingual Spoken Information Access Framework||Niklas Laxström
|WikiTalk||IWSDS 2014||English||18 January 2014||0||0|
|Avoimen suomenkielisen morfologian liittäminen Wikimedian hakujärjestelmään||Niklas Laxström||University of Helsinki||Finnish||1 January 2012||In my thesis I investigated the feasibility of using a Finnish morphology implementation with the Lucene search system. With the same Lucene-search package that is used by the Wikimedia Foundation I built two search indexes: one with the existing Porter stemming algorithm and the other one with morphological analysis. The corpus I used was the current text dump of Finnish Wikipedia. [...] See http://laxstrom.name/blag/2012/02/13/exploring-the-states-of-open-source-search-stack-supporting-finnish/||9||0|
|Concept-based document classification using Wikipedia and value function||Pekka Malo
|Journal of the American Society for Information Science and Technology||English||2011||In this article, we propose a new concept-based method for document classification. The conceptual knowledge associated with the words is drawn from Wikipedia. The purpose is to utilize the abundant semantic relatedness information available in Wikipedia in an efficient value function-based query learning algorithm. The procedure learns the value function by solving a simple linear programming problem formulated using the training documents. The learning involves a step-wise iterative process that helps in generating a value function with an appropriate set of concepts (dimensions) chosen from a collection of concepts. Once the value function is formulated, it is utilized to make a decision between relevance and irrelevance. The value assigned to a particular document from the value function can be further used to rank the documents according to their relevance. Reuters newswire documents have been used to evaluate the efficacy of the procedure. An extensive comparison with other frameworks has been performed. The results are promising.||0||0|
|Semantic Content Filtering with Wikipedia and Ontologies||Pekka Malo
|English||2010||The use of domain knowledge is generally found to improve query efficiency in content filtering applications. In particular, tangible benefits have been achieved when using knowledge-based approaches within more specialized fields, such as medical free texts or legal documents. However, the problem is that sources of domain knowledge are time-consuming to build and equally costly to maintain. As a potential remedy, recent studies on Wikipedia suggest that this large body of socially constructed knowledge can be effectively harnessed to provide not only facts but also accurate information about semantic concept-similarities. This paper describes a framework for document filtering, where Wikipedia's concept-relatedness information is combined with a domain ontology to produce semantic content classifiers. The approach is evaluated using Reuters RCV1 corpus and TREC-11 filtering task definitions. In a comparative study, the approach shows robust performance and appears to outperform content classifiers based on Support Vector Machines (SVM) and C4.5 algorithm.||17||0|
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