Abstract
Relationships semantically connect entities and thus it is crucial to identify them when analysing texts in order to understand and interpret the content correctly. Only with extracted relations, a deeper text understanding (e.g., recognising the who, when, where of a medical event and the temporal and causal relationships between events) is possible. We have seen in the analysis of the medical social media language in Chap. 6 and in the analysis of mapping quality of existing information extraction tools, that more complex sentence structures occur in medical social media data. The tools failed in analysing meanings of verbs, which would be crucial to automatically analyse and process this data for example for knowledge gathering or generating semantic structures from medical social media. In this chapter, we will describe one possible approach to relation extraction coming from the field of Web Mining and study its relevance and performance on medical social media texts.
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Denecke, K. (2015). Relation Extraction. In: Health Web Science. Health Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-20582-3_9
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DOI: https://doi.org/10.1007/978-3-319-20582-3_9
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