The amount of text that is generated every day is increasing dramatically. This tremendous
volume of mostly unstructured text cannot be simply processed and perceived by computers …

In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for
sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) …

We propose a unified neural network architecture and learning algorithm that can be applied
to various natural language processing tasks including part-of-speech tagging, chunking …

We describe the CoNLL-2003 shared task: language-independent named entity recognition.
We give background information on the data sets (English and German) and the evaluation …

We analyze some of the fundamental design challenges and misconceptions that underlie
the development of an efficient and robust NER system. In particular, we address issues …

A recently proposed lattice model has demonstrated that words in character sequence can
provide rich word boundary information for character-based Chinese NER model. In this …

Many tasks involve predicting a large number of variables that depend on each other as well
as on other observed variables. Structured prediction methods are essentially a combination …

One of the most important issues in machine learning is whether one can improve the
performance of a supervised learning algorithm by including unlabeled data. Methods that …

Mineral named entity recognition (MNER) is the extraction for the specific types of entities
from unstructured Chinese mineral text, which is a prerequisite for building a mineral …

In this paper we describe the CoNLL-2005 shared task on Semantic Role Labeling. We
introduce the specification and goals of the task, describe the data sets and evaluation …