Raw-to-End Name Entity Recognition in Social Media
Table 4: Model Performance with Perfect Tokenization (¯¯¯¯¯Δ is the average performance difference to those with three system tokenizers in Table 2).
['[EMPTY]', 'TNT F1', 'TNT ¯¯¯¯¯Δ', 'BTC F1', 'BTC ¯¯¯¯¯Δ']
Importance of Pre-processing. One can easily observe that pre-processing plays a crucial role on the performance. For example, switching the tokenizer from NLTK to spaCy or Stanford CoreNLP leads to a drop of more than 20 absolute F1 points. We further conduct experiments to verify our intuition to conduct NER in the raw-to-end manner. Specifically, we construct models with gold-tokenized training data and evaluate their performance with NLTK, which achieves the best performance among all three tokenizers. It verifies our intuition to build the raw-to-end framework.