PUMA publications for /user/jaeschke/disambiguationhttps://puma.uni-kassel.de/user/jaeschke/disambiguationPUMA RSS feed for /user/jaeschke/disambiguation2024-03-29T01:39:16+01:00Disambiguating toponyms in newshttps://puma.uni-kassel.de/bibtex/2de574cf3bff3a3748fcd9bd5a9a0f3d1/jaeschkejaeschke2012-10-03T09:34:09+02:00disambiguation extraction geo map news toponym <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Eric Garbin" itemprop="url" href="/author/Eric%20Garbin"><span itemprop="name">E. Garbin</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Inderjeet Mani" itemprop="url" href="/author/Inderjeet%20Mani"><span itemprop="name">I. Mani</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing</span>, </em></span><em>Seite <span itemprop="pagination">363--370</span>. </em><em>Stroudsburg, PA, USA, </em><em><span itemprop="publisher">Association for Computational Linguistics</span>, </em>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Wed Oct 03 09:34:09 CEST 2012Stroudsburg, PA, USAProceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing363--370Disambiguating toponyms in news2005disambiguation extraction geo map news toponym This research is aimed at the problem of disambiguating toponyms (place names) in terms of a classification derived by merging information from two publicly available gazetteers. To establish the difficulty of the problem, we measured the degree of ambiguity, with respect to a gazetteer, for toponyms in news. We found that 67.82% of the toponyms found in a corpus that were ambiguous in a gazetteer lacked a local discriminator in the text. Given the scarcity of human-annotated data, our method used unsupervised machine learning to develop disambiguation rules. Toponyms were automatically tagged with information about them found in a gazetteer. A toponym that was ambiguous in the gazetteer was automatically disambiguated based on preference heuristics. This automatically tagged data was used to train a machine learner, which disambiguated toponyms in a human-annotated news corpus at 78.5% accuracy.Incorporating User Feedback into Name Disambiguation of Scientific Cooperation Networkhttps://puma.uni-kassel.de/bibtex/296f2ae8551126527c2dfe69c8fa22f6c/jaeschkejaeschke2012-06-20T09:53:58+02:00cirg collective computing disambiguation extraction human ie information intelligence social <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yuhua Li" itemprop="url" href="/author/Yuhua%20Li"><span itemprop="name">Y. Li</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Aiming Wen" itemprop="url" href="/author/Aiming%20Wen"><span itemprop="name">A. Wen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Quan Lin" itemprop="url" href="/author/Quan%20Lin"><span itemprop="name">Q. Lin</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ruixuan Li" itemprop="url" href="/author/Ruixuan%20Li"><span itemprop="name">R. Li</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Zhengding Lu" itemprop="url" href="/author/Zhengding%20Lu"><span itemprop="name">Z. Lu</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Web-Age Information Management</span>, </em><em>Volume 6897 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin/Heidelberg, </em></span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Wed Jun 20 09:53:58 CEST 2012Berlin/HeidelbergWeb-Age Information Management454--466Lecture Notes in Computer ScienceIncorporating User Feedback into Name Disambiguation of Scientific Cooperation Network68972011cirg collective computing disambiguation extraction human ie information intelligence social In scientific cooperation network, ambiguous author names may occur due to the existence of multiple authors with the same name. Users of these networks usually want to know the exact author of a paper, whereas we do not have any unique identifier to distinguish them. In this paper, we focus ourselves on such problem, we propose a new method that incorporates user feedback into the model for name disambiguation of scientific cooperation network. Perceptron is used as the classifier. Two features and a constraint drawn from user feedback are incorporated into the perceptron to enhance the performance of name disambiguation. Specifically, we construct user feedback as a training stream, and refine the perceptron continuously. Experimental results show that the proposed algorithm can learn continuously and significantly outperforms the previous methods without introducing user interactions.