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Naoaki Okazaki

6 أوراق في مجموعة PaperMetrix

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أوراق هذا المؤلف

  1. A Joint Neural Model for Fine-Grained Named Entity Classification of Wikipedia Articles

    2017 · IEICE Transactions on Information and Systems

    This paper addresses the task of assigning labels of fine-grained named entity (NE) types to Wikipedia articles. Information of NE types are useful when extracting knowledge of NEs from natural language text. It is common …

  2. Construction of a Corpus of Rhetorical Devices in Slogans and Structural Analysis of Antitheses

    2021 · ACM Transactions on Asian and Low-Resource Language Information Processing

    An advertising slogan is a sentence that expresses a product or a work of art in a straightforward manner and is used for advertising and publicity. Moving the consumer's mind and attracting their interest can …

  3. Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer

    2024 · arXiv (Cornell University)

    Document-level Relation Extraction (DocRE) is the task of extracting all semantic relationships from a document. While studies have been conducted on English DocRE, limited attention has been given to DocRE in non-English languages. This work …

  4. Distributional Properties of Subword Regularization

    2024 · arXiv (Cornell University)

    Subword regularization, used widely in NLP, improves model performance by reducing the dependency on exact tokenizations, augmenting the training corpus, and exposing the model to more unique contexts during training. BPE and MaxMatch, two popular …

  5. Neural Headline Generation on Abstract Meaning Representation

    2016

    Neural network-based encoder-decoder models are among recent attractive methodologies for tackling natural language generation tasks. This paper investigates the usefulness of structural syntactic and semantic information additionally incorporated in a baseline neural attention-based model. We …

  6. Improving Truthfulness of Headline Generation

    2020

    Most studies on abstractive summarization report ROUGE scores between system and reference summaries. However, we have a concern about the truthfulness of generated summaries: whether all facts of a generated summary are mentioned in the …