Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC 2016, Portoroz, Slovenia, May 23-28, 2016 |
| Publisher | European Language Resources Association (ELRA) |
| ISBN (Print) | 9782951740891 |
| Publication status | Published - 2016 |
Cite this
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Crowdsourcing Salient Information from News and Tweets. / Inel, O.A.; Caselli, T.; Aroyo, L.M.
Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC 2016, Portoroz, Slovenia, May 23-28, 2016. European Language Resources Association (ELRA), 2016.Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Crowdsourcing Salient Information from News and Tweets
AU - Inel, O.A.
AU - Caselli, T.
AU - Aroyo, L.M.
PY - 2016
Y1 - 2016
N2 - The increasing streams of information pose challenges to both humans and machines. On the one hand, humans need to identify relevant information and consume only the information that lies at their interests. On the other hand, machines need to understand the information that is published in online data streams and generate concise and meaningful overviews. We consider events as primefactors to query for information and generate meaningful context. The focus of this paper is to acquire empirical insights for identifying salience features in tweets and news about a target event, i.e., the event of “whaling”. We first derive a methodology to identify suchfeatures by building up a knowledge space of the event enriched with relevant phrases, sentiments and ranked by their novelty. Weapplied this methodology on tweets and we have performed preliminary work towards adapting it to news articles. Our results show that crowdsourcing text relevance, sentiments and novelty (1) can be a main step in identifying salient information, and (2) provides a deeperand more precise understanding of the data at hand compared to state-of-the-art approaches.
AB - The increasing streams of information pose challenges to both humans and machines. On the one hand, humans need to identify relevant information and consume only the information that lies at their interests. On the other hand, machines need to understand the information that is published in online data streams and generate concise and meaningful overviews. We consider events as primefactors to query for information and generate meaningful context. The focus of this paper is to acquire empirical insights for identifying salience features in tweets and news about a target event, i.e., the event of “whaling”. We first derive a methodology to identify suchfeatures by building up a knowledge space of the event enriched with relevant phrases, sentiments and ranked by their novelty. Weapplied this methodology on tweets and we have performed preliminary work towards adapting it to news articles. Our results show that crowdsourcing text relevance, sentiments and novelty (1) can be a main step in identifying salient information, and (2) provides a deeperand more precise understanding of the data at hand compared to state-of-the-art approaches.
M3 - Conference contribution
SN - 9782951740891
BT - Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC 2016, Portoroz, Slovenia, May 23-28, 2016
PB - European Language Resources Association (ELRA)
ER -