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Researcher profile

Nathan Schneider

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Frame-Semantic Role Labeling with Heterogeneous Annotations

    2015

    Meghana Kshirsagar, Sam Thomson, Nathan Schneider, Jaime Carbonell, Noah A. Smith, Chris Dyer. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing …

  2. Adposition Supersenses v2.

    2017 · arXiv (Cornell University)

    This document describes an inventory of 50 semantic labels designed to characterize the use of adpositions and case markers at a somewhat coarse level of granularity. Version 2 is a revision of the supersense inventory …

  3. BERT Has Uncommon Sense: Similarity Ranking for Word Sense BERTology

    2021 · arXiv (Cornell University)

    An important question concerning contextualized word embedding (CWE) models like BERT is how well they can represent different word senses, especially those in the long tail of uncommon senses. Rather than build a WSD system …

  4. Are UD Treebanks Getting More Consistent? A Report Card for English UD

    2023 · arXiv (Cornell University)

    Recent efforts to consolidate guidelines and treebanks in the Universal Dependencies project raise the expectation that joint training and dataset comparison is increasingly possible for high-resource languages such as English, which have multiple corpora. Focusing …

  5. Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

    2018 · Figshare

    We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools …

  6. Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters

    2018 · Figshare

    We consider the problem of part-of-speech tagging for informal, online conversational text. We systematically evaluate the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy. With these features, our system …