Abstract
One of the core aims of semantic search is to directly present users with information instead of lists of documents. Various entity-oriented tasks have been or are being considered, including entity search and related entity finding. In the context of digital libraries for computational humanities, we consider another task, network extraction: given an input entity and a document collection, extract related entities from the collection and present them as a network. We develop a combined approach for entity network extraction that consists of a co-occurrence-based approach to association finding and a machine learning-based approach to relation extraction. We evaluate our approach by comparing the results on a ground truth obtained using a pooling method.
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Reinanda, R., Utama, M., Steijlen, F., de Rijke, M. (2013). Entity Network Extraction Based on Association Finding and Relation Extraction. In: Aalberg, T., Papatheodorou, C., Dobreva, M., Tsakonas, G., Farrugia, C.J. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2013. Lecture Notes in Computer Science, vol 8092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40501-3_16
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DOI: https://doi.org/10.1007/978-3-642-40501-3_16
Publisher Name: Springer, Berlin, Heidelberg
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