Natural language processing (NLP) is useful for handling text and speech, and sequence
labeling plays an important role by automatically analyzing a sequence (text) to assign …

This paper proposes a method for handwritten Chinese/Japanese text (character string)
recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order …

Natural language processing is a technique to process data such as text and speech. Some
fundamental research includes named-entity recognition, which recognizes name entities …

Dependencies among neighboring labels in a sequence are important sources of
information for sequence labeling and segmentation. However, only first-order …

We introduce a new objective function for pool-based Bayesian active learning with
probabilistic hypotheses. This objective function, called the policy Gibbs error, is the …

The natural language processing (NLP) denotes a technique used to process data such as
text and speech. Some of the fundamental research in NLP includes the named entity …

The Dravidian family is one of the most widely spoken set of languages in the world, yet
there are very few annotated resources available to NLP researchers. To remedy this, we …

Abstract We consider\em Conditional Random Fields (CRFs) with pattern-based potentials
defined on a chain. In this model the energy of a string (labeling) x_1\ldots x_n is the sum of …

A popular framework in supervised machine learning is passive learning, where a large
amount of training data are randomly gathered and labeled by human annotators and then …

We consider two models for the sequence labeling (tagging) problem. The first one is a
Pattern-Based Conditional Random Field (PB), in which the energy of a string (chain …