Natural Language Processing for Music Knowledge Discovery
Table 2: Precision, Recall and F-measure of entity linking approaches.
['Approach', 'Precision', 'Recall', '[ITALIC] F-measure']
To determine which approach performs better, three artist biographies were manually annotated, having a total number of 49 annotated entities. We observe that applying NER to entities of the Person category worsens performance significantly, as recall suddenly decreases by half. After manually analyzing false negatives, we observed that this is caused because many artist names have definite articles between name and surname (e.g., de, del), and this is not recognized correctly by the NER system. In addition, many artists have a nickname that is not interpreted as a Person entity by the NER system. The best approach is the third one (NER to LOC), where NER output is used only for Locations, which is slightly better than the first one (no NER) in terms of precision. This is due to the fact that many artists have a town name as a surname or as part of his or her nickname. Therefore, applying entity linking directly to text spans is misclassifying Person entities as Location entities. Thus, by adding a previous step of NER to Location entities we have increased the overall performance, as it can be seen on the F-measure values.