Information extraction from research papers using conditional random fields

By Fuchun Peng, Andrew McCallum. 2006

267 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Plan explicability and predictability for robot task planning (2015, background): “Furthermore, as an extension to our work, when robots cannot find an explicable plan that is also cost efficient, they need to explain the situation.”
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields (2015, background): “, 2003], semantic role labeling [Cohn and Blunsom, 2005], topic modeling [Zhu and Xing, 2010], information extraction [Peng and McCallum, 2006], shallow parsing [Sha and Pereira, 2003], named-entity recognition [Settles, 2004], as well as a host of other applications in natural language processing a”
Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields (2010, methodology): “Conditional Random Fields (CRF) (Lafferty et al., 2001) have been successfully applied to several IE tasks in the past (Peng and McCallum, 2006).”
Document Summarization Using Conditional Random Fields (2007, methodology): “N log(PΛ(Yj |Xj)) (2) To avoid overfitting, some regularization methods are employed [Peng and McCallum, 2006].”
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications (2017, methodology): “, 2009), extracting author and citation graphs (Peng and McCallum, 2006; Chaimongkol et al., 2014; Sim et al., 2015) or a combination of those (Radev and Abu-Jbara, 2012; Gollapalli and Li, 2015; Guo et al.”
Document Retrieval Model Through Semantic Linking (2017, methodology): “In [27, 28], CRFs are used as a method for combining a diverse set of features.”
Sequence Classification Restricted Boltzmann Machines With Gated Units (2020, methodology): “…(SVM struct ) [31], max-margin Markov network (M3N) [29], averaged perceptron [32], search-based structured prediction (SEARN) [33], CRF [12], [34], HMM [11], LogitBoost [35], TreeCRF [20], RTDRBM [25] (using the inference algorithm proposed in this article to adapt to the sequence labeling…”
Research-paper recommender systems: a literature survey (2015, unknown): “[256-260], or citation extraction and matching [261].”
From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems (2017, background): “…2003; Etzioni et al., 2004; Cafarella et al., 2005; Chang et al., 2006; Banko et al., 2007; Etzioni et al., 2008; Mitchell et al., 2015) and scientiﬁc publication data (Shah et al., 2003; Peng and McCallum, 2006; Saleem and Latif, 2012), work on IE from educational material is much more sparse.”