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AI zooms in on highly influential citations
Oren EtzioniNature • 2017 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 PowerSIGIR • 2017 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 CallanWWW • 2017 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 EtzioniJCDL • 2017 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 HovyACL • 2017 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…The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
Waleed Ammar, Matthew E. Peters, Chandra Bhagavatula, and Russell PowerSemEval • 2017 This paper describes our submission for the ScienceIE shared task (SemEval-2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several…PDFFigures 2.0: Mining Figures from Research Papers
Christopher Clark and Santosh DivvalaJCDL • 2016 Figures and tables are key sources of information in many scholarly documents. However, current academic search engines do not make use of figures and tables when semantically parsing documents or presenting document summaries to users. To facilitate these…Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization
Shih-Wen Huang, Jonathan Bragg, Isaac Cowhey, Oren Etzioni, and Daniel S. WeldCSCW • 2016 Successful online communities (e.g., Wikipedia, Yelp, and StackOverflow) can produce valuable content. However, many communities fail in their initial stages. Starting an online community is challenging because there is not enough content to attract a…Identifying Meaningful Citations
Marco Valenzuela, Vu Ha, and Oren EtzioniAAAI • Workshop on Scholarly Big Data • 2015 We introduce the novel task of identifying important citations in scholarly literature, i.e., citations that indicate that the cited work is used or extended in the new effort. We believe this task is a crucial component in algorithms that detect and follow…Looking Beyond Text: Extracting Figures, Tables and Captions from Computer Science Papers
Christopher Clark and Santosh DivvalaAAAI • Workshop on Scholarly Big Data • 2015 Identifying and extracting figures and tables along with their captions from scholarly articles is important both as a way of providing tools for article summarization, and as part of larger systems that seek to gain deeper, semantic understanding of these…