Machine Learning for Information Extraction in Informal Domains

By Dayne Freitag. Machine-mediated learning, 2000

401 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/58de638505046e7de5fe7cc0660b4c6d79247488

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In Artificial Intelligence

Citations in the corpus

Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling (2005, methodology): “This dataset was developed as part of Dayne Freitag’s dissertation research, and is described in Freitag (1998). A new version is available for download in XML format at http://nlp.”
Learning in Markov Random Fields with Contrastive Free Energies (2005, methodology): “This data set is due to Dayne Freitag [2], and has been used in much previous work.”
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data (2004, methodology): “The data is a collection of 485 e-mail messages announcing seminars at Carnegie Mellon University, gathered by Freitag (1998), and has been the subject of much previous work using a wide variety of learning methods.”
An Introduction to Conditional Random Fields for Relational Learning (2007, methodology): “This data set is due to Freitag [1998], and has been used in much previous work.”
Finding advertising keywords on web pages (2006, background): “Analogous to keyword extraction, information extraction is also a problem that aims to extract or label phrases given a document [8, 2, 19, 5].”
Composition of Conditional Random Fields for Transfer Learning (2005, neutral): “We evaluate joint decoding on a collection of 485 e-mail messages announcing seminars at Carnegie Mellon University, gathered by Freitag (1998).”
Probabilistic Reasoning for Entity & Relation Recognition (2002, methodology): “Recognizing and classifying entities and relations in text data is a key task in many NLP problems such as information extraction (IE) (Califf and Mooney, 1999; Freitag, 2000; Roth and Yih, 2001), question answering (QA) (Voorhees, 2000) and story comprehension (Hirschman et al.”
Web Data Extraction, Applications and Techniques: A Survey (2012, methodology): “In the following we shortly describe some Web Data Extraction approaches relying on Machine Learning algorithms [78,16,92,109,46,60,93].”
The Tradeoffs Between Open and Traditional Relation Extraction (2008, methodology): “Freitag (2000) used linear regression to model the relationship be-tween the conﬁdence of several inductive learning algorithms – rote learning, Naive Bayes, grammatical inference and a relational rule learning – and the probability that a prediction is correct.”
Minimum-Risk Training of Approximate CRF-Based NLP Systems (2012, methodology): “We utilize the CMU seminar announcement corpus of Freitag (2000) consisting of emails with seminar announcements.”