--- title: "Who cites Parsing entire discourses as very — 7 citing papers" description: "7 papers of the Elamur Research library cite «Parsing entire discourses as very long strings: Capturing topic continuity in grounded language…" canonical: "https://research.elamur.ai/free/en/papers/luong-2013-parsing-entire-discourses-as-very/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/luong-2013-parsing-entire-discourses-as-very/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Parsing entire discourses as very long strings…](https://md.research.elamur.ai/free/en/papers/luong-2013-parsing-entire-discourses-as-very/) / cited by # Who cites «Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning» 7 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning»](https://md.research.elamur.ai/free/en/papers/luong-2013-parsing-entire-discourses-as-very/) 1. **[Recursive Deep Models for Discourse Parsing](https://md.research.elamur.ai/free/en/papers/li-2014-recursive-deep-models-discourse-parsing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiwei Li, Rumeng Li, Eduard Hovy*2014 · 206 citations* 2. **[Prefix Parsing is Just Parsing](https://md.research.elamur.ai/free/en/papers/pasti-2026-prefix-parsing-is-just-parsing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Clemente Pasti, Andreas Opedal, Timothy J. O'Donnell, et al.*2026* 5 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/luong-2013-parsing-entire-discourses-as-very/) — Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning