Analysis of Named Entity Recognitionand Linking for Tweets
Table 3: Named entity recognition performance over the evaluation partition of the Ritter dataset.
['[BOLD] System', '[BOLD] Per-entity F1  [BOLD] Location', '[BOLD] Per-entity F1  [BOLD] Misc', '[BOLD] Per-entity F1  [BOLD] Org', '[BOLD] Per-entity F1  [BOLD] Person', '[BOLD] Overall  [BOLD] P', '[BOLD] Overall  [BOLD] R', '[BOLD] Overall  [BOLD] F1']
We can see that conventional tools (i.e., those trained on newswire) perform poorly in this genre, and thus microblog domain adaptation is crucial for good NER. However, when compared to results typically achieved on longer news and blog texts, state-of-the-art in microblog NER is still lacking. Consequently, there is a significant proportion of missed entity mentions and false positives. A small performance benefit is realised, but recall remains low. It is worth noting that ANNIE is largely gazetteer-based, and so less sensitive to some types of noise, including wrong POS tags. Balancing this advantage is the limited recall of gazetteers when faced with previously unseen items, visible here. However, statistical machine learning-based tools do not reach good levels of recall on tweets either.