Paper: Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media

ACL ID P13-1024
Title Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Many current Natural Language Process- ing [NLP] techniques work well assum- ing a large context of text as input data. However they become ineffective when applied to short texts such as Twitter feeds. To overcome the issue, we want to find a related newswire document to a given tweet to provide contextual support for NLP tasks. This requires robust model- ing and understanding of the semantics of short texts. The contribution of the paper is two-fold: 1. we introduce the Linking-Tweets-to- News task as well as a dataset of linked tweet-news pairs, which can benefit many NLP applications; 2. in contrast to previ- ous research which focuses on lexical fea- tures within the short texts (text-to-word information), we propose a graph based latent variable model that models the in- ter short t...