Overview of all concepts

Overview of all concepts

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Background: In the information extraction and natural language processing domain, accessible datasets are crucial to reproduce and compare results. Publicly available implementations and tools can serve as benchmark and facilitate the development of more complex applications. However, in the context of clinical text processing the number of accessi...

Contexts in source publication

Context 1
... concepts provide more detailed information to the other concepts, such as dosing, local or time information. An overview of the concept schema is provided in Table 1 and includes a short definition and examples. ...
Context 2
... overall best performing model uses a combination of word, Flair and Pool embeddings, unfortunately resulting in a model with the largest size of nearly 6GB. Table 10 shows the results of the custom Flair embeddings model on concept level. The table also shows the overall (including training and development) frequency of each concept in the dataset. ...
Context 3
... supports our hypothesis that concept information is beneficial to relation extraction from clinical text, as the context lacks important linguistic information. Table 11 shows the detailed results (first run) of the default word + concept embeddings + relative offsets model, including the overall frequency of the different relations. Similarly to the concepts, the distribution of the relations is unbalanced. ...
Context 4
... Table 11 presents the micro avg. F1 IAA scores of the annotators. ...