Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF

We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging.Peer reviewe

A GRU-based pipeline approach for word-sentence segmentation and punctuation restoration in English

This is an accepted manuscript of an article published by IEEE in Proceedings of 2021 International Conference on Asian Language Processing (IALP) on 20 Jan 2022. Available online at https://doi.org/10.1109/IALP54817.2021.9675269 The accepted version of the publication may differ from the final published version.In this study, we propose a Gated Recurrent Unit (GRU) model to restore the following features: word and sentence boundaries, periods, commas, and capitalisation for unformatted English text. We approach feature restoration as a binary classification task where the model learns to predict whether a feature should be restored or not. A pipeline approach is proposed, in which only one feature (word boundary, sentence boundary, punctuation, capitalisation) is restored in each component of the pipeline model. To optimise the model, we conducted a grid search on the parameters. The effect of changing the order of the pipeline is also investigated experimentally; PERIODS > COMMAS > SPACES > CASING yielded the best result. Our findings highlight several specifcaction points with optimisation potential to be targeted in follow-up research

The Hmong Medical Corpus: a biomedical corpus for a minority language

Biomedical communication is an area that increasingly benefits from natural language processing (NLP) work. Biomedical named entity recognition (NER) in particular provides a foundation for advanced NLP applications, such as automated medical question-answering and translation services. However, while a large body of biomedical documents are available in an array of languages, most work in biomedical NER remains in English, with the remainder in official national or regional languages. Minority languages so far remain an underexplored area. The Hmong language, a minority language with sizable populations in several countries and without official status anywhere, represents an exceptional challenge for effective communication in medical contexts. Taking advantage of the large number of government-produced medical information documents in Hmong, we have developed the first named entity-annotated biomedical corpus for a resource-poor minority language. The Hmong Medical Corpus contains 100,535 tokens with 4554 named entities (NEs) of three UMLS semantic types: diseases/syndromes, signs/symptoms, and body parts/organs/organ components. Furthermore, a subset of the corpus is annotated for word position and parts of speech, representing the first such gold-standard dataset publicly available for Hmong. The methodology presented provides a readily reproducible approach for the creation of biomedical NE-annotated corpora for other resource-poor languages