Accurate Information Extraction from Research Papers using Conditional Random Fields

By Fuchun Peng, Andrew McCallum. North American Chapter of the Association for Computational Linguistics, 2004

338 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/75ce8d8d72ef16fc2aeb4887a51cbb26cdef3c77

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Citations in the corpus

Mining knowledge from text using information extraction (2005, methodology): “Several earlier IE systems used generative HMM models [4; 31]; however, discriminately-trained CRF models have recently been shown to have an advantage over HMM’s [54; 65].”
ParsCit: an Open-source CRF Reference String Parsing Package (2008, methodology): “In later work by the same group, Peng and McCallum (2004) training to compute a model (Ng, 2004).”
An Introduction to Conditional Random Fields for Relational Learning (2007, methodology): “Recently there has been an explosion of interest in CRFs, with successful applications including text processing [Taskar et al., 2002, Peng and McCallum, 2004, Settles, 2005, Sha and Pereira, 2003], bioinformatics [Sato and Sakakibara, 2005, Liu et al., 2005], and computer vision [He et al., 2004,…”
Chinese Segmentation and New Word Detection using Conditional Random Fields (2004, unknown): “See Peng and McCallum (2004) for more details and further experiments.”
Semi-supervised multi-task learning of structured prediction models for web information extraction (2011, methodology): “[5, 1, 29, 30, 41] apply sequential models like Hidden Markov Models (HMM), Conditional Random Fields (CRF) and Markov Logic Networks (MLN) to segment instances such as citations and addresses.”
GROBID: Combining Automatic Bibliographic Data Recognition and Term Extraction for Scholarship Publications (2009, methodology): “We have applied Conditional Random Fields to this task following the approach of [1] implemented with the Mallet toolkit [2], based on approx.”
Open Information Extraction Using Wikipedia (2010, methodology): “Occurrence-statistical models (Agichtein and Gra-vano, 2000; M. Ciaramita, 2005), graphical models (Peng and McCallum, 2004; Poon and Domin-gos, 2008), and kernel-based methods (Bunescu and R.Mooney, 2005) have been studied.”
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields (2007, background): “…outperform generative techniques in areas such as natural language processing (Lafferty et al. 2001; Sha and Pereira 2003), information extraction (Peng and McCallum 2004; Kristjannson et al. 2004), web page classiﬁcation (Taskar et al. 2002), and computer vision (Kumar and Herbert 2003;…”
The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics (2008, background): “High accuracy reference segmentation is thus a signiﬁcant challenge, and one that has already attracted considerable attention (see, for example, (Peng and McCallum, 2004)).”
An Introduction to Conditional Random Fields (2010, background): “Successful applications have included text processing [89, 107, 108], bioinformatics [106, 65], and computer vision [43, 53].”
Applying Conditional Random Fields to Japanese Morphological Analysis (2004, methodology): “…CRFs have been reported recently in part-of-speech tagging (Lafferty et al., 2001), shallow parsing (Sha and Pereira, 2003), named entity recognition (Mc-Callum and Li, 2003), Chinese word segmentation (Peng et al., 2004), and Information Extraction (Pinto et al., 2003; Peng and McCallum, 2004).”
An Analysis of Active Learning Strategies for Sequence Labeling Tasks (2008, background): “CORA (Peng and McCallum, 2004) consists of two collections: a set of research paper headers annotated for entities such as title, author, and institution; and a collection of references annotated with BibTeX fields such as journal, year, and publisher.”
Location-Based Activity Recognition using Relational Markov Networks (2005, background): “[Peng and McCallum, 2004] demonstrated that better accuracy can been achieved if feature-dependent variances are used.”