Linear street extraction using a Conditional Random Field model
نویسندگان
چکیده
منابع مشابه
Linear Street Extraction Using a Conditional Random Field Model
A novel method for extracting linear streets from a street network is proposed where a linear street is defined as a sequence of connected street segments having a shape similar to a straight line segment. Specifically a given street network is modeled as a Conditional Random Field (CRF) where the task of extracting linear streets corresponds to performing learning and inference with respect to...
متن کاملRule Extraction from A Trained Conditional Random Field Model
Conditional Random Field (CRF) has proven to be highly successful for sequence labeling problems like part of speech tagging, segmentation etc. However, the model acts like a black box, providing no insight into what is learned. We propose a system for rule extraction from CRF to assist comprehensibility of the model. Experiments on POS tagging and chunking problem in English are performed as c...
متن کاملA Novel Approach to Conditional Random Field-based Named Entity Recognition using Persian Specific Features
Named Entity Recognition is an information extraction technique that identifies name entities in a text. Three popular methods have been conventionally used namely: rule-based, machine-learning-based and hybrid of them to extract named entities from a text. Machine-learning-based methods have good performance in the Persian language if they are trained with good features. To get good performanc...
متن کاملImage Labeling and Segmentation using Hierarchical Conditional Random Field Model
The use of hierarchical Conditional Random Field model deal with the problem of labeling images . At the time of labeling a new image, selection of the nearest cluster and using the related CRF model to label this image. When one give input image, one first use the CRF model to get initial pixel labels then finding the cluster with most similar images. Then at last relabeling the input image by...
متن کاملTowards Definition Extraction Using Conditional Random Fields
Definition Extraction (DE) and terminology are contributing to help structuring the overwhelming amount of information available. This article presents KESSI (Knowledge Extraction System for Scientific Interviews), a multilingual domainindependent machine-learning approach to the extraction of definitional knowledge, specifically oriented to scientific interviews. The DE task was approached as ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Spatial Statistics
سال: 2015
ISSN: 2211-6753
DOI: 10.1016/j.spasta.2015.10.003