Named Entity Mention Grounding Task
(Redirected from Named Entity Resolution Task)
- See: Referencer Grounding Task, Entity Reference Resolution Task.
- (Han & Zhao, 2009) ⇒ Xianpei Han, and Jun Zhao. (2009). “Named Entity Disambiguation by Leveraging Wikipedia Semantic Knowledge.” In: Proceedings of the Eighteenth Conference on Information and Knowledge Management (CIKM 2009) doi:10.1145/1645953.1645983
- (Jijkoun et al., 2008) ⇒ Valentin Jijkoun, Mahboob Alam Khalid, Maarten Marx, and Maarten de Rijke\n. (2008). “Named Entity Normalization in User Generated Content.” In: Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data (AND 2008). doi:10.1145/1390749.1390755
- QUOTE: When we consider the special case of this problem for natural language texts, we have to recognize entities in a text and resolve these references either to entities that exist within the document or to real-world entities. These two steps constitute the named entity normalization (NEN) problem. … We consider the NEN (named entity normalization) task within the setting of user generated content (UGC), such as blogs, discussion forums, or comments left behind by readers of online documents. For this type of textual data, the NEN task is particularly important within the settings of media and reputation analysis (which motivated the work reported here) and of intelligence gathering.
- (Witte et al., 2007) ⇒ René Witte, Thomas Kappler, and Christopher J. O. Baker. (2007). “Ontology Design for Biomedical Text Mining." Book Chapter in: Semantic Web". doi:10.1007/978-0-387-48438-9
- QUOTE: As a final step in NE detection, many entities need to be grounded with respect to an external resource, like a database. This is especially important for most biological entities, which have corresponding entries in various databases, e.g., Swiss-Prot for proteins. When further information is needed for downstream analysis tasks, like the automatic processing of amino acid sequences, grounding the textual entity to a unique database entry (e.g., assigning a Swiss-Prot ID to a protein entity) is a mandatory prerequisite. Thus, even if an entity is correctly detected from an NLP perspective, it might still be ambiguous with respect to such an external resource (or not exist at all), which makes it useless for further automated processing until the entity has been grounded.
- (Podowski et al., 2005) ⇒ Raf M. Podowski, J. G. Cleary, N.T. Goncharoff, G. Amoutzias, and W.S. Hayes WS. (2005). “Suregene, a scalable system for automated term disambiguation of gene and protein names. J. Bioinform Comput Biol. 3(3) PMID:16108092