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Viewing 10 papers from 2019 in Semantic Scholar
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    • EMNLP 2019
      Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Daniel S. Weld
      As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to…  (More)
    • EMNLP 2019
      Iz Beltagy, Kyle Lo, Arman Cohan
      Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised…  (More)
    • EMNLP 2019
      Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, Omer Levy
      We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span…  (More)
    • EMNLP 2019
      Mandar Joshi, Omer Levy, Daniel S. Weld, Luke Zettlemoyer
      We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but…  (More)
    • ACL 2019
      Christine Betts, Joanna Power, Waleed Ammar
      We introduce GrapAL (Graph database of Academic Literature), a versatile tool for exploring and investigating a knowledge base of scientific literature, that was semi-automatically constructed using NLP methods. GrapAL satisfies a variety of use cases and information needs requested by researchers…  (More)
    • ACL • BioNLP Workshop 2019
      Mark Neumann, Daniel King, Iz Beltagy, Waleed Ammar
      Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust…  (More)
    • JAMA 2019
      Sergey Feldman, Waleed Ammar, Kyle Lo, Elly Trepman, Madeleine van Zuylen, Oren Etzioni
      Importance: Analyses of female representation in clinical studies have been limited in scope and scale. Objective: To perform a large-scale analysis of global enrollment sex bias in clinical studies. Design, Setting, and Participants: In this cross-sectional study, clinical studies from published…  (More)
    • arXiv 2019
      Lucy Lu Wang, Gabriel Stanovsky, Luca Weihs, Oren Etzioni
      A comprehensive and up-to-date analysis of Computer Science literature (2.87 million papers through 2018) reveals that, if current trends continue, parity between the number of male and female authors will not be reached in this century. Under our most optimistic projection models, gender parity is…  (More)
    • NAACL 2019
      Arman Cohan, Waleed Ammar, Madeleine van Zuylen, Field Cady
      Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose a multitask approach to incorporate information in…  (More)
    • NAACL 2019
      Iz Beltagy, Kyle Lo, Waleed Ammar
      In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant…  (More)