Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 21. Januar 2019)
1Doval, Y. ; Gómez-Rodríguez, C.: Comparing neural- and N-gram-based language models for word segmentation.
In: Journal of the Association for Information Science and Technology. 70(2019) no.2, S.187-197.
Abstract: Word segmentation is the task of inserting or deleting word boundary characters in order to separate character sequences that correspond to words in some language. In this article we propose an approach based on a beam search algorithm and a language model working at the byte/character level, the latter component implemented either as an n-gram model or a recurrent neural network. The resulting system analyzes the text input with no word boundaries one token at a time, which can be a character or a byte, and uses the information gathered by the language model to determine if a boundary must be placed in the current position or not. Our aim is to use this system in a preprocessing step for a microtext normalization system. This means that it needs to effectively cope with the data sparsity present on this kind of texts. We also strove to surpass the performance of two readily available word segmentation systems: The well-known and accessible Word Breaker by Microsoft, and the Python module WordSegment by Grant Jenks. The results show that we have met our objectives, and we hope to continue to improve both the precision and the efficiency of our system in the future.
Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24082.
2Vilares, J. ; Alonso, M.A. ; Doval, Y. ; Vilares, M.: Studying the effect and treatment of misspelled queries in Cross-Language Information Retrieval.
In: Information processing and management. 52(2016) no.4, S.646-657.
Abstract: General graph random walk has been successfully applied in multi-document summarization, but it has some limitations to process documents by this way. In this paper, we propose a novel hypergraph based vertex-reinforced random walk framework for multi-document summarization. The framework first exploits the Hierarchical Dirichlet Process (HDP) topic model to learn a word-topic probability distribution in sentences. Then the hypergraph is used to capture both cluster relationship based on the word-topic probability distribution and pairwise similarity among sentences. Finally, a time-variant random walk algorithm for hypergraphs is developed to rank sentences which ensures sentence diversity by vertex-reinforcement in summaries. Experimental results on the public available dataset demonstrate the effectiveness of our framework.
Inhalt: Vgl.: http://www.sciencedirect.com/science/article/pii/S0306457315001478.
Themenfeld: Multilinguale Probleme