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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 OpenFraming: open-sourced tool for computational framing analysis of multilingual data Sun, Yimeng; Tofu, David Assefa; Halim, Edward Edberg. - : Association for Computational Linguistics, 2021 BASE Show details 2 "Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks ... Rasooli, Mohammad Sadegh; Callison-Burch, Chris; Wijaya, Derry Tanti. - : arXiv, 2021 BASE Show details 3 Majority Voting with Bidirectional Pre-translation For Bitext Retrieval ... Jones, Alex; Wijaya, Derry Tanti. - : arXiv, 2021 BASE Show details 4 Resolving pronouns in Twitter streams: context can help! Wijaya, Derry Tanti; Andy, Anietie; Callison-Burch, Chris. - : Association for Computational Linguistics, 2020 BASE Show details 5 Winter is here: summarizing Twitter streams related to pre-scheduled events Andy, Anietie; Wijaya, Derry Tanti; Callison-Burch, Chris. - : Association for Computational Linguistics, 2019 Abstract: Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7. ; Published version URL: https://doi.org/10.18653/v1/w19-3412 https://hdl.handle.net/2144/40711 BASE Hide details 6 Detecting frames in news headlines and its application to analyzing news framing trends surrounding U.S. gun violence Liu, Siyi; Guo, Lei; Mays, Kate. - : Association for Computational Linguistics, 2019 BASE Show details

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