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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    This repository contains reusable Machine Translation (MT) by the MasakhaneNLP Community. MASAKHANE is an research effort for NLP for African languages that is OPEN SOURCE, CONTINENT-WIDE, DISTRIBUTED and ONLINE. This repository houses the models for Machine Translation for African languages. See masakhane-mt-current-models.csv for current model information. This repository was created by the Masakhane Web Translate Team. You can see some of the models in action on http://translate.masakhane.io/

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2021
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2021
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2021
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2021
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2021
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2021
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2021
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2021
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2021
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2021
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2021
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2021
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Adel Tahri;

    ABSTRACT Question Answering (QA) is an area of natural language processing research aimed at providing human users with a convenient and natural interface for accessing information. Nowadays, the need to develop accurate systems gains more importance due to available structured knowledge-bases and the continuous demand to access information rapidly and efficiently. In This paper we propose a new architecture to develop a factoid question answering system based on the DBpedia ontology and the DBpedia extraction framework. We use the SVM learning machine algorithm to train the systems question classifier to achieve a high accuracy ratio. The design and implementation steps are covered with sufficient details. In addition, tests and experiment results of the developed system are presented with a short discussion about the systems efficiency

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Article . 2013
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Article . 2013
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Article . 2013
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Article . 2013
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Radim Řehůřek; Petr Sojka;

    Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM). In this paper, we identify a gap in existing implementations of many of the popular algorithms, which is their scalability and ease of use. We describe a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion. Within this framework, we implement several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation, in a way that makes them completely independent of the training corpus size. Particular emphasis is placed on straightforward and intuitive framework design, so that modifications and extensions of the methods and/or their application by interested practitioners are effortless. We demonstrate the usefulness of our approach on a real-world scenario of computing document similarities within an existing digital library DML-CZ.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Conference object . 2010
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Conference object . 2010
      License: CC BY
      Data sources: ZENODO
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Joseph Gatto; Sarah M. Preum;

    An increasing number of people now rely on online platforms to meet their health information needs. Thus identifying inconsistent or conflicting textual medical information has become a safety-critical task. While there are ongoing efforts to detect fake news and misinformation, they often utilize only statistical features and assume some information is incorrect. However, many individuals receive medical advice that is accurate in the context of one diagnosis yet conflicting or inconsistent with each other, e.g., people suffering from diabetes and hypertension often receive conflicting medical advice on diet. Given two pieces of medical advice, the goal of the medical conflict detection task is to detect and categorize the type of conflict or inconsistency. It is a challenging task, as (i) automatically identifying and categorizing conflicts requires a deeper understanding of the semantics of the text, and (ii) the amount of available data is quite limited. Prior research focusing on conflict detection uses semantic rule based solutions that require a significant amount of manual effort for data annotation and rule generation. In this paper, we formulate medical conflict detection as a multilabel multiclass classification task in the context of neural language models. We explore the applicability of a state-of-the-art language model, namely BERT, to solve this problem. We introduce the Hierarchical Pair-Wise Encoder for Text Classification (HiPErText), which augments BERT by utilizing Abstract Meaning Representation (AMR) graphs for enhanced conflict detection. HiPErText shows promising results for such a resource-constrained text classification task and outperforms BERT by a 40% increase in F1 score on average. We also demonstrate how we can approach BERT-level performance on our pairwise inference task using only information extracted from AMR graphs.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Preprint . 2022
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Preprint . 2022
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Preprint . 2022
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Preprint . 2022
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Marivate, Vukosi; Njini, Daniel; Madodonga, Andani; Lastrucci, Richard; +2 Authors

    # The Vuk'uzenzele South African Multilingual Corpus [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7598539.svg)](https://doi.org/10.5281/zenodo.7598539) Github: https://github.com/dsfsi/vukuzenzele-nlp ## About dataset The dataset contains editions from the South African government magazine Vuk'uzenzele. Data was scraped from PDFs that have been placed in the [data/raw](data/raw/) folder. The PDFS were obtained from the [Vuk'uzenzele website](https://www.vukuzenzele.gov.za/). The datasets contain government magazine editions in 11 languages, namely: | Language | Code | Language | Code | |------------|-------|------------|-------| | English | (eng) | Sepedi | (sep) | | Afrikaans | (afr) | Setswana | (tsn) | | isiNdebele | (nbl) | Siswati | (ssw) | | isiXhosa | (xho) | Tshivenda | (ven) | | isiZulu | (zul) | Xitstonga | (tso) | | Sesotho | (nso) | ### Number of Aligned Pairs with Cosine Similarity Score >= 0.65 | src_lang | trg_lang | num_aligned_pairs | |----------|----------|-------------------| | ven | zul | 186 | | ssw | xho | 1965 | | sep | xho | 279 | | nbl | zul | 227 | | nso | tsn | 1279 | | nso | tso | 1491 | | tsn | zul | 1346 | | afr | eng | 1369 | | eng | ssw | 1601 | | afr | ssw | 1496 | | nbl | ssw | 264 | | tso | zul | 1758 | | afr | zul | 1384 | | eng | zul | 1888 | | ssw | tsn | 1263 | | sep | tsn | 302 | | nso | xho | 1248 | | sep | tso | 324 | | ssw | tso | 1657 | | tsn | ven | 235 | | eng | nbl | 153 | | nso | sep | 349 | | afr | nbl | 359 | | nbl | ven | 657 | | eng | ven | 243 | | afr | ven | 281 | | tso | ven | 256 | | ven | xho | 215 | | eng | tsn | 1380 | | afr | tsn | 1076 | | nso | ssw | 1132 | | eng | tso | 2016 | | afr | tso | 1139 | | xho | zul | 1895 | | tsn | xho | 1209 | | sep | zul | 223 | | nbl | xho | 204 | | ssw | zul | 2161 | | afr | xho | 1363 | | eng | xho | 1354 | | tso | xho | 1485 | | sep | ssw | 219 | | nbl | tso | 215 | | tsn | tso | 1570 | | nso | zul | 1247 | | nbl | tsn | 140 | | eng | sep | 276 | | afr | sep | 394 | | ssw | ven | 217 | | sep | ven | 1140 | | afr | nso | 962 | | eng | nso | 1721 | | nbl | nso | 151 | | nbl | sep | 843 | | nso | ven | 262 | The dataset is present in several forms on the repo. Generally the dataset is split by edition, eg. `2020-01-ed1` The data directory is broken down as follows ``` ./data ├── external # Data external to this repo ├── interim # I am not really sure - looks like interim in regards to processed. ├── processed # The data from scraping the raw pdfs ├── raw # The raw pdfs of the Vuk'uzenzele magazine ├── sentence_align_output # The output (csv) of the sentence alignment with LASER language encoders └── simple_align_output # The output (csv) of a simple one to one sentence alignment ``` The dataset is split by edition in the [data/processed](data/processed/) folder. Authors ------- - Vukosi Marivate - [@vukosi](https://twitter.com/vukosi) - Andani Madodonga - Daniel Njini - Richard Lastrucci Citation -------- Vukosi Marivate, Andani Madodonga, Daniel Njini, Richard Lastrucci, Isheanesu Dzingirai . **The Vuk'uzenzele South African Multilingual Corpus**, 2023 > @inproceedings{lastrucci-etal-2023-preparing, title = "Preparing the Vuk{'}uzenzele and {ZA}-gov-multilingual {S}outh {A}frican multilingual corpora", author = "Richard Lastrucci and Isheanesu Dzingirai and Jenalea Rajab and Andani Madodonga and Matimba Shingange and Daniel Njini and Vukosi Marivate", booktitle = "Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.rail-1.3", pages = "18--25" } > @dataset{marivate_vukosi_2023_7598540, author = {Marivate, Vukosi and Njini, Daniel and Madodonga, Andani and Lastrucci, Richard and Dzingirai, Isheanesu}, title = {The Vuk'uzenzele South African Multilingual Corpus}, month = feb, year = 2023, publisher = {Zenodo}, doi = {10.5281/zenodo.7598539}, url = {https://doi.org/10.5281/zenodo.7598539} } Licences ------- * License for Data - [CC 4.0 BY SA](LICENSE.data.md) * Licence for Code - [MIT License](LICENSE.md)

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    ZENODO
    Dataset . 2023
    License: CC BY SA
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    Authors: Marivate, Vukosi; Mots'Oehli, Moseli; Wagner, Valencia; Lastrucci, Richard; +1 Authors

    PuoBERTa + PuoBERTaJW300: Setswana Language Models A Roberta-based language model specially designed for Setswana, using the new PuoData dataset (PuoBERTa) and PuoData + JW300 TSN (PuoBERTaJW300) Cite @inproceedings{marivate2023puoberta, title = {PuoBERTa: Training and evaluation of a curated language model for Setswana}, author = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai}, year = {2023}, booktitle= {Artificial Intelligence Research. SACAIR 2023. Communications in Computer and Information Science}, url= {https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17}, keywords = {NLP}, preprint_url = {https://arxiv.org/abs/2310.09141}, dataset_url = {https://github.com/dsfsi/PuoBERTa}, software_url = {https://huggingface.co/dsfsi/PuoBERTa} } Model Details Model Description This is a masked language model trained on Setswana corpora, making it a valuable tool for a range of downstream applications from translation to content creation. It's powered by the PuoData dataset to ensure accuracy and cultural relevance. Developed by: Vukosi Marivate (@vukosi), Moseli Mots'Oehli (@MoseliMotsoehli) , Valencia Wagner, Richard Lastrucci and Isheanesu Dzingirai Model type: RoBERTa Model Language(s) (NLP): Setswana License: CC BY 4.0 Usage Use this model filling in masks or finetune for downstream tasks. Here's a simple example for masked prediction: from transformers import RobertaTokenizer, RobertaModel # Load model and tokenizer model = RobertaModel.from_pretrained('dsfsi/PuoBERTa') tokenizer = RobertaTokenizer.from_pretrained('dsfsi/PuoBERTa') Downstream Use Downstream Performance MasakhaPOS Performance of models on the MasakhaPOS downstream task. Model Test Performance Multilingual Models AfroLM 83.8 AfriBERTa 82.5 AfroXLMR-base 82.7 AfroXLMR-large 83.0 Monolingual Models NCHLT TSN RoBERTa 82.3 PuoBERTa 83.4 PuoBERTa+JW300 84.1 MasakhaNER Performance of models on the MasakhaNER downstream task. Model Test Performance (f1 score) Multilingual Models AfriBERTa 83.2 AfroXLMR-base 87.7 AfroXLMR-large 89.4 Monolingual Models NCHLT TSN RoBERTa 74.2 PuoBERTa 78.2 PuoBERTa+JW300 80.2 Dataset We used the PuoData dataset, a rich source of Setswana text, ensuring that our model is well-trained and culturally attuned. Citation Information Bibtex Reference @inproceedings{marivate2023puoberta, title = {PuoBERTa: Training and evaluation of a curated language model for Setswana}, author = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai}, year = {2023}, booktitle= {Artificial Intelligence Research. SACAIR 2023. Communications in Computer and Information Science}, url= {https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17}, keywords = {NLP}, preprint_url = {https://arxiv.org/abs/2310.09141}, dataset_url = {https://github.com/dsfsi/PuoBERTa}, software_url = {https://huggingface.co/dsfsi/PuoBERTa} } Contributing Your contributions are welcome! Feel free to improve the model. Model Card Authors Vukosi Marivate Model Card Contact For more details, reach out or check our website. Email: vukosi.marivate@cs.up.ac.za Enjoy exploring Setswana through AI!

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    Authors: Theron, Lamont; Marivate, Vukoisi;

    # South African Bank Risk Dataset ## About Dataset This repository is an initial pipeline for reading, processing, labelling and classifying unstructured annual reports of South African (SA) banks with the aim of identifying financial risk. It leveraged work by the Corporate Financial Information Environment-Final Report Structure Extractor (CFIE–FRSE) of El-Haj et al. which created a corpus of annual reports of United Kingdom (UK) companies. ## About Data collection methodology A register of banks licensed in SA was used to download annual reports from company websites and online portals. Company structures, trading practices and branding complicated the collection. The South African Bank of Athens Limited (‘SABA’) is an example, trading as Grobank after acquisition by GroCapital Holdings Proprietary Limited (‘GroCapital Holdings’). Subsidiary and group companies were analysed as separate entities. This applied to ABSA, Investec, Nedbank and Standard Bank. A UK report was selected as reference to validate pre-processing results against the CFIE–FRSE tool. Pre-porcessing also mapped reports to companies and years, and labeled reports as positive or negative with respect to risk. Data was prepared by extracting text, counting all words and those in wordlists, and validating results against the reference. Processing included Bag of Words, word embedding, feature scaling and topic analysis. Modelling entailed various classifiers for which results were compared and the best performing models were applied in a prediction use case. ## Description of the data Of the potential 297 annual reports from 2009 to 2019, 258 were sourced with the balance not found or the bank was not operational that year. Only 7 reports comprised multiple documents so for simplicity each document was treated as a report. ## Repository Organisation ------------ ├── data │ ├── interim │ │ ├── wordlists <- Lists of keywords/substrings to extract linguistic features │ │ │ ├── causal.txt <- Causal reasoning wordlist relating to performance commentary, based on a composite wordlist from El-Haj et al. │ │ │ ├── causalMartin50.txt <- Causal reasoning wordlist based on El-Haj et al. │ │ │ ├── causalMartinAll.txt <- Causal reasoning wordlist based on El-Haj et al. │ │ │ ├── ForwardLooking.txt <- Forwardlooking wordlist based on the list proposed by Hussainey et al. │ │ │ ├── forwardLookingNew.txt <- Forwardlooking wordlist based on an updated version of the list proposed by Hussainey et al. │ │ │ ├── HenryNeg2006.txt <- Negative wordlist based on Henry (2006) │ │ │ ├── HenryNeg2008.txt <- Negative wordlist based on Henry (2008) │ │ │ ├── HenryPos2006.txt <- Positive wordlist based on Henry (2006) │ │ │ ├── HenryPos2008.txt <- Positive wordlist based on Henry (2008) │ │ │ ├── LMnegative.txt <- Negative wordlist based on Loughran and McDonald │ │ │ ├── LMpositive.txt <- Positive wordlist based on Loughran and McDonald │ │ │ ├── newStrategy.txt <- Wordlist for identifying strategy-related commentary based on El-Haj et al. │ │ │ ├── performance.txt <- Wordlist for identifying performance-related commentary based on El-Haj et al. │ │ │ └── Uncertainty.txt <- Uncertainty wordlist based on Loughran and McDonald │ │ ├── coMap.csv <- Reference data: Company mapping file │ │ ├── docMap.csv <- Reference data: Document mapping file │ │ ├── fileNoMap.csv <- Output data: PDF files read that were not in the document mapping file │ │ ├── header.csv <- Output data: Header text extracted from PDF files │ │ ├── match.csv <- Keywords to match and classify headers based on El-Haj et al. │ │ └── MatchToC.txt <- Keywords to identify Table of Content header │ ├── processed │ │ ├── docReadability.csv <- Readability results per document without the text │ │ ├── pageBlocks.csv <- Text blocks per page per document with word counts per page and per wordlist │ │ ├── pageText.csv <- Text per page per document with word counts per page and per wordlist │ │ ├── pageTextRef.csv <- Text per page of reference report to validate word counts with CFIE–FRSE │ │ ├── prob_test_LR.csv <- Probability of risk on test dataset reports predicted by Logistic Regression │ │ └── prob_test_SVMa.csv <- Probability of risk on test dataset reports predicted by Support Vector Machine with auto gamma │ └── raw │ ├── Annual Reports <- PDF files downloaded from internet websites │ │ ├── Bank <- Annual reports read and processed to create the dataset │ │ └── Insurer <- Empty folder for insurer reports to be stored in future │ └── Other <- Other risk-related reports downloaded from internet websites ├── notebooks <- Python code │ ├── colab <- Code for Google Colaboratory and cloud runtime │ │ ├── 1_0_Colab Import.ipynb <- Extract PDF text, convert booklets, count words by page and write pageText.csv │ │ ├── 2_0_Colab EDA.ipynb <- Exploratory Data Analysis (incl. Chi-Square) and write docReadability.csv │ │ ├── 3_0_Colab Classifier.ipynb <- Loop through raw and stemmed/lemmatized tokens as well as classifiers │ │ ├── 4_0_Colab Classifier Applied LR.ipynb <- Logistic Regression prediction, LIME, feature selection and write prob_test_LR.csv │ │ └── 4_1_Colab Classifier Applied SVMa.ipynb <- Support Vector Machine (with auto gamma) prediction, LIME, feature selection and write prob_test_SVMa.csv │ └── jupyter <- Code for Python 3 and local runtime e.g. using Jupyter or JupyterLab │ │── 1_0_Import.ipynb <- Extract PDF text, convert booklets, count words by page and write pageText.csv │ └── 1_1_Import Count Blocks.ipynb <- Extract PDF text, convert booklets, count words by page and write pageBlocks.csv ├── .gitignore ├── LICENSE └── README.md ## Online Repository link * [Zenodo Data Repository](https://zenodo.org/record/6769996) - Link to the data repository. ## Authors * **Lamont Theron** * **Vukosi Marivate** - [@vukosi](https://twitter.com/vukosi) See also the list of [contributors](https://github.com/dsfsi/za-bank-risk/contributors) who participated in this project. ## License Data is Licensed under CC 4.0 BY Code is Licences under MIT License.

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    Authors: Athira P. M., Sreeja;

    Question answering (QA) system aims at retrieving precise information from a large collection of documents against a query. This paper describes the architecture of a Natural Language Question Answering (NLQA) system for a specific domain based on the ontological information, a step towards semantic web question answering. The proposed architecture defines four basic modules suitable for enhancing current QA capabilities with the ability of processing complex questions. The first module was the question processing, which analyses and classifies the question and also reformulates the user query. The second module allows the process of retrieving the relevant documents. The next module processes the retrieved documents, and the last module performs the extraction and generation of a response. Natural language processing techniques are used for processing the question and documents and also for answer extraction. Ontology and domain knowledge are used for reformulating queries and identifying the relations. The aim of the system is to generate short and specific answer to the question that is asked in the natural language in a specific domain. We have achieved 94 % accuracy of natural language question answering in our implementation.

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    Authors: Palmero Aprosio, Alessio; Moretti, Giovanni;

    In this we paper present Tint, an easy-to-use set of fast, accurate and extendable Natural Language Processing modules for Italian. It is based on Stanford CoreNLP and is freely available as a standalone software or a library that can be integrated in an existing project.

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    Authors: ANURGO PACHECO, Albert;

    The dataset includes 35.305 court decisions related to UK asylum requests. The original owner of the data is the UK's Upper Tribunal Immigration and Asylum Chamber (UTIAC). The court decisions were scraped from https://tribunalsdecisions.service.gov.uk/utiac on 28th October 2021. For each court decision the dataset includes the original text of the court as well as a number of explanatory fields that have been generated following an information extraction process. The dataset includes the following fields: 'Case title:', 'Appellant name:', 'Status of case:', 'Hearing date:', 'Promulgation date:', 'Publication date:', 'Last updated on:', 'Country:', 'Judges:', 'Document', 'Reference', 'Download', 'File', 'String', 'ID', 'Code label:', 'Heard at:', 'Decision:', 'Nationality:', 'Representation:', 'Appellant:', 'Respondent:', 'Decision label:', 'Appellant entity:', 'Respondent entity:', 'Country code', 'Keywords', 'Country guidance:', 'Case Notes:', 'Categories:']

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    This repository contains reusable Machine Translation (MT) by the MasakhaneNLP Community. MASAKHANE is an research effort for NLP for African languages that is OPEN SOURCE, CONTINENT-WIDE, DISTRIBUTED and ONLINE. This repository houses the models for Machine Translation for African languages. See masakhane-mt-current-models.csv for current model information. This repository was created by the Masakhane Web Translate Team. You can see some of the models in action on http://translate.masakhane.io/

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Software . 2021
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Software . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Adel Tahri;

    ABSTRACT Question Answering (QA) is an area of natural language processing research aimed at providing human users with a convenient and natural interface for accessing information. Nowadays, the need to develop accurate systems gains more importance due to available structured knowledge-bases and the continuous demand to access information rapidly and efficiently. In This paper we propose a new architecture to develop a factoid question answering system based on the DBpedia ontology and the DBpedia extraction framework. We use the SVM learning machine algorithm to train the systems question classifier to achieve a high accuracy ratio. The design and implementation steps are covered with sufficient details. In addition, tests and experiment results of the developed system are presented with a short discussion about the systems efficiency

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    Article . 2013
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    Data sources: Datacite
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    Article . 2013
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      Article . 2013
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Radim Řehůřek; Petr Sojka;

    Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM). In this paper, we identify a gap in existing implementations of many of the popular algorithms, which is their scalability and ease of use. We describe a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion. Within this framework, we implement several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation, in a way that makes them completely independent of the training corpus size. Particular emphasis is placed on straightforward and intuitive framework design, so that modifications and extensions of the methods and/or their application by interested practitioners are effortless. We demonstrate the usefulness of our approach on a real-world scenario of computing document similarities within an existing digital library DML-CZ.

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    Conference object . 2010
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Joseph Gatto; Sarah M. Preum;

    An increasing number of people now rely on online platforms to meet their health information needs. Thus identifying inconsistent or conflicting textual medical information has become a safety-critical task. While there are ongoing efforts to detect fake news and misinformation, they often utilize only statistical features and assume some information is incorrect. However, many individuals receive medical advice that is accurate in the context of one diagnosis yet conflicting or inconsistent with each other, e.g., people suffering from diabetes and hypertension often receive conflicting medical advice on diet. Given two pieces of medical advice, the goal of the medical conflict detection task is to detect and categorize the type of conflict or inconsistency. It is a challenging task, as (i) automatically identifying and categorizing conflicts requires a deeper understanding of the semantics of the text, and (ii) the amount of available data is quite limited. Prior research focusing on conflict detection uses semantic rule based solutions that require a significant amount of manual effort for data annotation and rule generation. In this paper, we formulate medical conflict detection as a multilabel multiclass classification task in the context of neural language models. We explore the applicability of a state-of-the-art language model, namely BERT, to solve this problem. We introduce the Hierarchical Pair-Wise Encoder for Text Classification (HiPErText), which augments BERT by utilizing Abstract Meaning Representation (AMR) graphs for enhanced conflict detection. HiPErText shows promising results for such a resource-constrained text classification task and outperforms BERT by a 40% increase in F1 score on average. We also demonstrate how we can approach BERT-level performance on our pairwise inference task using only information extracted from AMR graphs.

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    Preprint . 2022
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    Preprint . 2022
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