This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features and ...
Named Entity Recognition and Classification is being studied for last two decades. Since semantic features take huge amount of training time and are slow in inference, the existing...
Siddhartha Jonnalagadda, Robert Leaman, Trevor Coh...
Background: The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of a...
Due to the lack of annotated data sets, there are few studies on machine learning based approaches to extract named entities (NEs) in clinical text. The 2009 i2b2 NLP challenge is...
In this paper, we describe some experiments in large-scale Information Extraction (IE) focusing on book texts. We investigate the scalability of IE techniques to full-sized books,...