Abstract
Named Entity rEcognition and Linking (NEEL) from text
is an essential task in many Natural Language Processing
(NLP) applications because it enables a better understanding
of the content. However in the context of Social Media,
NEEL is challenging due to the higher level of writing
mistakes, fast language dynamics and often lack of context.
To this end, we adapted Kanopy an unsupervised
graph-based topic disambiguation system to be used for
the task of NEEL in the domain of Twitter, a fast-paced
micro-blogging platform. We describe the design of our solution
and report the results obtained by our system using the
official corpus of Tweets for the NEEL 2016 Challenge [10].
| Original language | English (Ireland) |
|---|---|
| Title of host publication | WWW 2016, Workshop Track: 6th Workshop on Making Sense of Microposts |
| Place of Publication | Montréal, Canada |
| Publication status | Published - 1 Apr 2016 |
Authors (Note for portal: view the doc link for the full list of authors)
- Authors
- Torres-Tramón, P; Hromic, H; Walsh, B; R. Heravi, B; Hayes, C