Papers

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Viewing 141-150 of 155 papers
  • A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

    Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy SchwartzNAACL-HLT2018 Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research pur- poses (PeerRead v1), providing an opportunity to study this important artifact. The dataset…
  • Content-Based Citation Recommendation

    Chandra Bhagavatula, Sergey Feldman, Russell Power, Waleed AmmarNAACL-HLT2018 We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to…
  • Extracting Scientific Figures with Distantly Supervised Neural Networks

    Noah Siegel, Nicholas Lourie, Russell Power and Waleed AmmarJCDL2018 Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development of data-driven methods for scientific figure extraction. In this paper, we…
  • Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context

    Lucy L. Wang, Chandra Bhagavatula, M. Neumann, Kyle Lo, Chris Wilhelm, Waleed AmmarACL • Proceedings of the BioNLP 2018 Workshop2018 Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose…
  • Semi-supervised sequence tagging with bidirectional language models

    Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, and Russell PowerACL2017 Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive…
  • AI zooms in on highly influential citations

    Oren EtzioniNature2017 The number of times a paper is cited is a poor proxy for its impact (see P. Stephan et al. Nature 544, 411–412; 2017). I suggest relying instead on a new metric that uses artificial intelligence (AI) to capture the subset of an author's or a paper's essential…
  • End-to-End Neural Ad-hoc Ranking with Kernel Pooling

    Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell PowerSIGIR2017 This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to…
  • Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding

    Chenyan Xiong, Russell Power and Jamie CallanWWW2017 This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine, SemanticScholar.org, reveals that a major error source is its inability to…
  • Learning to Predict Citation-Based Impact Measures

    Luca Weihs and Oren EtzioniJCDL2017 Citations implicitly encode a community's judgment of a paper's importance and thus provide a unique signal by which to study scientific impact. Efforts in understanding and refining this signal are reflected in the probabilistic modeling of citation networks…
  • Ontology Aware Token Embeddings for Prepositional Phrase Attachment

    Pradeep Dasigi, Waleed Ammar, Chris Dyer, and Eduard HovyACL2017 Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent…