Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-task Learning

Daniel Hardt, Dirk Hovy, Sotiris Lamprinidis

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

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Abstrakt

Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, newssection prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns.
OriginalsprogEngelsk
TitelProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
RedaktørerEllen Riloff, David Chiang, Julia Hockenmaier, Tsujii Jun’ichi
Antal sider6
UdgivelsesstedBrussels
ForlagAssociation for Computational Linguistics
Publikationsdato2018
Sider659-664
StatusUdgivet - 2018
Begivenhed2018 Conference on Empirical Methods in Natural Language Processing - Square Meeting Center, Brussels, Belgien
Varighed: 31 okt. 20184 nov. 2018
http://emnlp2018.org/

Konference

Konference2018 Conference on Empirical Methods in Natural Language Processing
LokationSquare Meeting Center
LandBelgien
ByBrussels
Periode31/10/201804/11/2018
Internetadresse

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