  Applying natural language processing for mining and intelligent information
access to tweets (a form of microblog) is a challenging, emerging research
area. Unlike carefully authored news text and other longer content, tweets pose
a number of new challenges, due to their short, noisy, context-dependent, and
dynamic nature. Information extraction from tweets is typically performed in a
pipeline, comprising consecutive stages of language identification,
tokenisation, part-of-speech tagging, named entity recognition and entity
disambiguation (e.g. with respect to DBpedia). In this work, we describe a new
Twitter entity disambiguation dataset, and conduct an empirical analysis of
named entity recognition and disambiguation, investigating how robust a number
of state-of-the-art systems are on such noisy texts, what the main sources of
error are, and which problems should be further investigated to improve the
state of the art.
