Evaluation of experiments with NER-based filtering on news Sample Clauses

Evaluation of experiments with NER-based filtering on news text Given that the inconsistency of dbtype encoding disallows us from enhancing Spotlight results based on re-ranking, we experiment with filtering based on other parameters:  Spotlight own confidence threshold  the fact that a given Spotlight entity has also been identified as such by Stanford  the value of nertype  size of the NER not larger than the NEL The last parameter may be relevant in cases where entities without a Wikipedia link are misidentified by Spotlight. Example: Xxxxxxxx Xxxxxx [PERSON], analyzed by Spotlight as two wrong XXXx: Xxxxxxxx xxxx://xx.xxxxxxxxx.xxx/wiki/Cornelia_Cinna_minor Xxxxxx xxxx://xx.xxxxxxxxx.xxx/wiki/Keith_Haring Combination Precision in Detection Recall in Detection F-measure in Detection Accuracy in Classification of Detected Items DBSpotlight (c > 0.9) 0.3002 0.6871 0.4178 0.7639 Filtered DBSpotlight 0.9659 0.5906 0.7331 0.7809 Table 2: Results of filtering on the Xxxx-Xxxx (news) dataset The final combination of filtering parameters is shown in Figure 4: (!nertype.equals("PERSON") && confidence > 0.9) || (nertype.equals("PERSON") && size_ne <= sizedb && confidence > 0.6) Figure 4: News settings for NER-based filtered NEL Precision rises dramatically for filtered results, and importantly without a great loss in recall.
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