Neural conditional random fields

Estimating Rainfall with Neural Networks and Conditional Random Fields

Hidden Conditional Neural Fields for Continuous Phoneme Speech Recognition

In this paper, we propose Hidden Conditional Neural Fields (HCNF) for continuous phoneme speech recognition, which are a combination of Hidden Conditional Random Fields (HCRF) and a MultiLayer Perceptron (MLP), and inherit their merits, namely, the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. HCNF can incorporate many types of featu...

Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields

Most of the sequence tagging tasks in natural language processing require to recognize segments with certain syntactic role or semantic meaning in a sentence. They are usually tackled with Conditional Random Fields (CRFs), which do indirect word-level modeling over word-level features and thus cannot make full use of segment-level information. Semi-Markov Conditional Random Fields (Semi-CRFs) m...

Sum-Product Networks for Structured Prediction: Context-Specific Deep Conditional Random Fields

Linear-chain conditional random fields (LCCRFs) have been successfully applied in many structured prediction tasks. Many previous extensions, e.g. replacing local factors by neural networks, are computationally demanding. In this paper, we extend conventional LC-CRFs by replacing the local factors with sum-product networks, i.e. a promising new deep architecture allowing for exact and efficient...

Conditional Random Fields for Airborne Lidar Point Cloud Classification in Urban Area

Over the past decades, urban growth has been known as a worldwide phenomenon that includes widening process and expanding pattern. While the cities are changing rapidly, their quantitative analysis as well as decision making in urban planning can benefit from two-dimensional (2D) and three-dimensional (3D) digital models. The recent developments in imaging and non-imaging sensor technologies, s...

Conditional Random Fields for Automatic Punctuation

We model the relationship between sentences and their punctuation labels using conditional random fields. Some feature functions are hand-designed and others are generated by templates. We train the same model by stochastic gradient ascent, Collins Perceptron and contrastive divergence respectively and compare their performance. On the provided dataset, we achieve word-level accuracy of 94.56%....