We present novel kernels based on structured and unstructured features for reranking the N-best hypotheses of conditional random fields (CRFs) applied to entity extraction. The fo...
Truc-Vien T. Nguyen, Alessandro Moschitti, Giusepp...
In this paper, we present a new method for learning to finding translations and transliterations on the Web for a given term. The approach involves using a small set of terms and ...
Joseph Z. Chang, Jason S. Chang, Jyh-Shing Roger J...
— This paper describes a Markov random field (MRF) model with weighting parameters optimized by conditional random field (CRF) for on-line recognition of handwritten Japanese cha...
The goal in domain adaptation is to train a model using labeled data sampled from a domain different from the target domain on which the model will be deployed. We exploit unlabel...
Traditionally, machine learning approaches for information extraction require human annotated data that can be costly and time-consuming to produce. However, in many cases, there ...