Early experiments on automatic annotation of Portuguese medieval texts

This paper presents the challenges and solutions adopted to the lemmatization and part-of-speech (PoS) tagging of a corpus of Old Portuguese texts (up to 1525), to pave the way to the implementation of an automatic annotation of these Medieval texts. A highly granular tagset, previously devised for Modern Portuguese, was adapted to this end. A large text (similar to 155 thousand words) was manually annotated for PoS and lemmata and used to train an initial PoS-tagger model. When applied to two other texts, the resulting model attained 91.2% precision with a textual variant of the same text, and 67.4% with a new, unseen text. A second model was then trained with the data provided by the previous three texts and applied to two other unseen texts. The new model achieved a precision of 77.3% and 82.4%, respectively.info:eu-repo/semantics/submittedVersio

Early experiments on automatic annotation of Portuguese medieval texts

This paper presents the challenges and solutions adopted to the lemmatization and part-of-speech (PoS) tagging of a corpus of Old Portuguese texts (up to 1525), to pave the way to the implementation of an automatic annotation of these Medieval texts. A highly granular tagset, previously devised for Modern Portuguese, was adapted to this end. A large text (∼155 thousand words) was manually annotated for PoS and lemmata and used to train an initial PoS-tagger model. When applied to two other texts, the resulting model attained 91.2% precision with a textual variant of the same text, and 67.4% with a new, unseen text. A second model was then trained with the data provided by the previous three texts and applied to two other unseen texts. The new model achieved a precision of 77.3% and 82.4%, respectively.info:eu-repo/semantics/acceptedVersio

Boosting Named Entity Recognition with Neural Character Embeddings

Most state-of-the-art named entity recognition (NER) systems rely on handcrafted features and on the output of other NLP tasks such as part-of-speech (POS) tagging and text chunking. In this work we propose a language-independent NER system that uses automatically learned features only. Our approach is based on the CharWNN deep neural network, which uses word-level and character-level representations (embeddings) to perform sequential classification. We perform an extensive number of experiments using two annotated corpora in two different languages: HAREM I corpus, which contains texts in Portuguese; and the SPA CoNLL-2002 corpus, which contains texts in Spanish. Our experimental results shade light on the contribution of neural character embeddings for NER. Moreover, we demonstrate that the same neural network which has been successfully applied to POS tagging can also achieve state-of-the-art results for language-independet NER, using the same hyperparameters, and without any handcrafted features. For the HAREM I corpus, CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-score for the total scenario (ten NE classes), and by 7.2 points in the F1 for the selective scenario (five NE classes).Comment: 9 page

Methods for Amharic part-of-speech tagging

The paper describes a set of experiments involving the application of three state-of- the-art part-of-speech taggers to Ethiopian Amharic, using three different tagsets. The taggers showed worse performance than previously reported results for Eng- lish, in particular having problems with unknown words. The best results were obtained using a Maximum Entropy ap- proach, while HMM-based and SVM- based taggers got comparable results

A non-projective greedy dependency parser with bidirectional LSTMs

The LyS-FASTPARSE team presents BIST-COVINGTON, a neural implementation of the Covington (2001) algorithm for non-projective dependency parsing. The bidirectional LSTM approach by Kipperwasser and Goldberg (2016) is used to train a greedy parser with a dynamic oracle to mitigate error propagation. The model participated in the CoNLL 2017 UD Shared Task. In spite of not using any ensemble methods and using the baseline segmentation and PoS tagging, the parser obtained good results on both macro-average LAS and UAS in the big treebanks category (55 languages), ranking 7th out of 33 teams. In the all treebanks category (LAS and UAS) we ranked 16th and 12th. The gap between the all and big categories is mainly due to the poor performance on four parallel PUD treebanks, suggesting that some `suffixed' treebanks (e.g. Spanish-AnCora) perform poorly on cross-treebank settings, which does not occur with the corresponding `unsuffixed' treebank (e.g. Spanish). By changing that, we obtain the 11th best LAS among all runs (official and unofficial). The code is made available at https://github.com/CoNLL-UD-2017/LyS-FASTPARSEComment: 12 pages, 2 figures, 5 table