Parsing Tweets into Universal Dependencies
Table 3: Tokenizer comparison on the Tweebank v2 test set.
['[ITALIC] System', '[ITALIC] F1']
Experimental results. Our preliminary results showed that our model trained on the combination of UD_English-EWT and Tweebank v2 outperformed the one trained only on the UD_English-EWT or Tweebank v2, consistent with previous work on dialect treebank parsing Wang et al. So we trained our tokenizer on the training portion of Tweebank v2 combined with the UD_English-EWT training set and tested on the Tweebank v2 test set. We report F1 scores, combining precision and recall for token identification. Stanford CoreNLP Manning et al. et al. Compared with UDPipe, we use an LSTM instead of a GRU in our model and we also use a larger size for hidden units (64 vs. 20), which has stronger representational power. Our bi-LSTM tokenizer achieves the best accuracy among all these tokenizers. These results speak to the value of statistical modeling in tokenization for informal texts.