Raw-to-End Name Entity Recognition in Social Media
Table 2: Performance Comparison of Sequence Labeling Models and Tokenizers on Twitter NER Benchmarks. The reported numbers are F1 score.
['Tokenizer', 'Methods', 'TNT', 'BTC']
Specifically, we consider two variants of the proposed method: Neural-Char-CRF (NLTK) uses NLTK-based embedding alignment and Neural-Char-CRF (Match) uses string matching-based embedding alignment. We can observe a significant performance improvement of Neural-Char-CRF over baseline methods (by 3.70 and 6.65 absolute F1 gain on TNT and BTC respectively), which verifies the effectiveness of our proposed framework. Further analyses are discussed as below.