Question: What previous approaches did this method outperform?
Evidence: The POS tagging and lemmatization results are presented in Table TABREF44. The word2vec word embeddings (WE) considerably increase performance compared to the baseline, especially in POS tagging. When only Flair embeddings are added to the baseline, we also observe an improvement, but not as high. We hypothesise that the lower performance (in contrast with the results reported in BIBREF2) is caused by the size of the training data, because we train the word2vec WE on considerably larger dataset than the Czech Flair model. However, when WE and Flair embeddings are combined, performance moderately increases, demonstrating that the two embedding methods produce at least partially complementary representations.
Table TABREF44 compares our best model with state-of-the-art results on PDT 2.0 (note that some of the related work used only a subset of PDT 2.0 and/or utilized gold morphological annotation). To our best knowledge, research on PDT parsing was performed mostly in the first decade of this century, therefore even our baseline model substantially surpasses previous works. Our best model with contextualized embeddings achieves nearly 50% error reduction both in UAS and LAS.
Table TABREF47 shows the performance of analyzed embedding methods in a joint model performing POS tagging, lemmatization, and dependency parsing on Czech PDT UD 2.3 treebank. This treebank is derived from PDT 3.5 a-layer, with original POS tags kept in XPOS, and the dependency trees and lemmas modified according to UD guidelines.
Table TABREF47 shows NER results (F1 score) on CNEC 1.1 and CNEC 2.0. Our sequence-to-sequence (seq2seq) model which captures the nested entities, clearly surpasses the current Czech NER state of the art. Furthermore, significant improvement is gained when adding the contextualized word embeddings (BERT and Flair) as optional input to the LSTM encoder. The strongest model is a combination of the sequence-to-sequence architecture with both BERT and Flair contextual word embeddings.
FLOAT SELECTED: Table 2. POS tagging and lemmatization results (accuracy) on PDT 3.5. Bold indicates the best result, italics related work. †Reported on PDT 2.0, which has the same underlying corpus, with minor changes in morphological annotation (our model results differ at 0.1% on PDT 2.0).
FLOAT SELECTED: Table 4. Dependency tree parsing results on PDT 2.0 a-layer. Bold indicates the best result, italics related work. †Possibly using gold POS tags. ‡Results as of 23 Mar 2019.
FLOAT SELECTED: Table 5. Czech PDT UD 2.3 results for POS tagging (UPOS: universal POS, XPOS: languagespecific POS, UFeats: universal morphological features), lemmatization and dependency parsing (UAS, LAS, MLAS, and BLEX scores). Bold indicates the best result, italics related work.
FLOAT SELECTED: Table 6. Named entity recognition results (F1) on the Czech Named Entity Corpus. Bold indicates the best result, italics related work.
The POS tagging and lemmatization results are presented in Table TABREF44.
Table TABREF44 compares our best model with state-of-the-art results on PDT 2.0 (note that some of the related work used only a subset of PDT 2.0 and/or utilized gold morphological annotation).
Table TABREF47 shows the performance of analyzed embedding methods in a joint model performing POS tagging, lemmatization, and dependency parsing on Czech PDT UD 2.3 treebank.
Table TABREF47 shows NER results (F1 score) on CNEC 1.1 and CNEC 2.0.
FLOAT SELECTED: Table 2. POS tagging and lemmatization results (accuracy) on PDT 3.5. Bold indicates the best result, italics related work. †Reported on PDT 2.0, which has the same underlying corpus, with minor changes in morphological annotation (our model results differ at 0.1% on PDT 2.0).
FLOAT SELECTED: Table 4. Dependency tree parsing results on PDT 2.0 a-layer. Bold indicates the best result, italics related work. †Possibly using gold POS tags. ‡Results as of 23 Mar 2019.
FLOAT SELECTED: Table 5. Czech PDT UD 2.3 results for POS tagging (UPOS: universal POS, XPOS: languagespecific POS, UFeats: universal morphological features), lemmatization and dependency parsing (UAS, LAS, MLAS, and BLEX scores). Bold indicates the best result, italics related work.
FLOAT SELECTED: Table 6. Named entity recognition results (F1) on the Czech Named Entity Corpus. Bold indicates the best result, italics related work.
Answer: The answers are shown as follows:
* Table TABREF44
* Table TABREF44
* Table TABREF47
* Table TABREF47