In order to achieve state-of-the-art performance for part-of-speech(POS) tagging, the traditional systems require a significant amount of hand-crafted features and data pre-processing. In this thesis, we present a discriminative word embedding, character embedding and byte pair encoding (BPE) hybrid neural network architecture to implement a true end-to-end system without feature engineering and data pre-processing. The neural network architecture is a combination of bidirectional LSTM, CNNs, and CRF, which can achieve a state-of-the-art performance for a wide range of sequence labeling tasks. We evaluate our model on Universal Dependencies (UD) dataset for English, Spanish, and German POS tagging. It outperforms other models with 95.1%, 98.15%, and 93.43% accuracy on testing datasets respectively. Moreover, the largest improvements of our model appear on out-of-vocabulary corpora for Spanish and German. According to statistical significance testing, the improvements of English on testing and out-of-vocabulary corpora are not statistically significant. However, the improvements of the other more morphological languages are statistically significant on their corresponding corpora.

This bachelor's thesis in Computer Science explores the efficiency of an incremental re-tokenization algorithm in the context of BPE-trained SentencePiece models used in natural language processing. The thesis begins by underscoring the critical role of tokenization in NLP, particularly highlighting the complexities introduced by modifications in tokenized text. It then presents an incremental re-tokenization algorithm, detailing its development and evaluating its performance against a full text re-tokenization. Experimental results demonstrate that this incremental approach is more time-efficient than full re-tokenization, especially evident in large text datasets. This efficiency is attributed to the algorithm's localized re-tokenization strategy, which limits processing to text areas around modifications. The research concludes by suggesting that incremental re-tokenization could significantly enhance the responsiveness and resource efficiency of text-based applications, such as chatbots and virtual assistants. Future work may focus on predictive models to anticipate the impact of text changes on token stability and optimizing the algorithm for different text contexts.