Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
- J. Lafferty, A. McCallum, Fernando Pereira
- Computer ScienceInternational Conference on Machine Learning
- 28 June 2001
This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
A comparison of event models for naive bayes text classification
- A. McCallum, K. Nigam
- Computer ScienceAAAI Conference on Artificial Intelligence
- 1998
It is found that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes--providing on average a 27% reduction in error over the multi -variateBernoulli model at any vocabulary size.
Modeling Relations and Their Mentions without Labeled Text
- S. Riedel, Limin Yao, A. McCallum
- Computer ScienceECML/PKDD
- 20 September 2010
A novel approach to distant supervision that can alleviate the problem of noisy patterns that hurt precision by using a factor graph and applying constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in the authors' training KB.
Text Classification from Labeled and Unlabeled Documents using EM
- K. Nigam, A. McCallum, S. Thrun, Tom Michael Mitchell
- Computer ScienceMachine-mediated learning
- 1 May 2000
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents, and presents two extensions to the algorithm that improve classification accuracy under these conditions.
Automating the Construction of Internet Portals with Machine Learning
- A. McCallum, K. Nigam, Jason D. M. Rennie, K. Seymore
- Computer ScienceInformation retrieval (Boston)
- 21 July 2000
New research in reinforcement learning, information extraction and text classification that enables efficient spidering, the identification of informative text segments, and the population of topic hierarchies are described.
Maximum Entropy Markov Models for Information Extraction and Segmentation
- A. McCallum, D. Freitag, Fernando C Pereira
- Computer ScienceInternational Conference on Machine Learning
- 29 June 2000
A new Markovian sequence model is presented that allows observations to be represented as arbitrary overlapping features (such as word, capitalization, formatting, part-of-speech), and defines the conditional probability of state sequences given observation sequences.
Optimizing Semantic Coherence in Topic Models
- David Mimno, H. Wallach, E. Talley, Miriam Leenders, A. McCallum
- Computer ScienceConference on Empirical Methods in Natural…
- 27 July 2011
A novel statistical topic model based on an automated evaluation metric based on this metric that significantly improves topic quality in a large-scale document collection from the National Institutes of Health (NIH).
Topics over time: a non-Markov continuous-time model of topical trends
- Xuerui Wang, A. McCallum
- Computer ScienceKnowledge Discovery and Data Mining
- 20 August 2006
An LDA-style topic model is presented that captures not only the low-dimensional structure of data, but also how the structure changes over time, showing improved topics, better timestamp prediction, and interpretable trends.
An Introduction to Conditional Random Fields for Relational Learning
- Charles Sutton, A. McCallum
- Computer Science
- 2007
A solution to this problem is to directly model the conditional distribution p(y|x), which is sufficient for classification, and this is the approach taken by conditional random fields.
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