TY - GEN
T1 - Constructing an Alias List for Named Entities during an Event
AU - Andy, Anietie
AU - Dredze, Mark
AU - Rwebangira, Mugizi
AU - Callison-Burch, Chris
N1 - Publisher Copyright:
©2017 Association for Computational Linguistics.
PY - 2017
Y1 - 2017
N2 - In certain fields, real-time knowledge from events can help in making informed decisions. In order to extract pertinent realtime knowledge related to an event, it is important to identify the named entities and their corresponding aliases related to the event. The problem of identifying aliases of named entities that spike has remained unexplored. In this paper, we introduce an algorithm, EntitySpike, that identifies entities that spike in popularity in tweets from a given time period, and constructs an alias list for these spiked entities. EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event. Each entity is encoded as a vector using this temporal heuristic.We show how these entityvectors can be used to create a named entity alias list. We evaluated our algorithm on a dataset of temporally ordered tweets from a single event, the 2013 Grammy Awards show. We carried out various experiments on tweets that were published in the same time period and show that our algorithm identifies most entity name aliases and outperforms a competitive baseline.
AB - In certain fields, real-time knowledge from events can help in making informed decisions. In order to extract pertinent realtime knowledge related to an event, it is important to identify the named entities and their corresponding aliases related to the event. The problem of identifying aliases of named entities that spike has remained unexplored. In this paper, we introduce an algorithm, EntitySpike, that identifies entities that spike in popularity in tweets from a given time period, and constructs an alias list for these spiked entities. EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event. Each entity is encoded as a vector using this temporal heuristic.We show how these entityvectors can be used to create a named entity alias list. We evaluated our algorithm on a dataset of temporally ordered tweets from a single event, the 2013 Grammy Awards show. We carried out various experiments on tweets that were published in the same time period and show that our algorithm identifies most entity name aliases and outperforms a competitive baseline.
UR - http://www.scopus.com/inward/record.url?scp=85123206186&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123206186&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85123206186
T3 - 3rd Workshop on Noisy User-Generated Text, W-NUT 2017 - Proceedings of the Workshop
SP - 40
EP - 44
BT - 3rd Workshop on Noisy User-Generated Text, W-NUT 2017 - Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 3rd Workshop on Noisy User-Generated Text, W-NUT 2017
Y2 - 7 September 2017
ER -