Papers by Apostolos Kemos
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging (N19-1)
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| Challenge: | Character-level models of tokens are effective at dealing with within-token noise and out-of-vocabulary words. |
| Approach: | They propose to eliminate the need for tokenizers by using a character-level semi-Markov conditional random field that uses neural networks for its character and segment representations. |
| Outcome: | The proposed model outperforms state-of-the-art part-of speech taggers on a noisy English dataset. |