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1
Unifying dimensions in coherence relations: how various annotation frameworks are related
In: Corpus linguistics and linguistic theory. - Berlin ; New York : Mouton de Gruyter 17 (2021) 1, 1-71
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2
DiscAlign for Penn and RST Discourse Treebanks
Demberg, Vera; Asr, Fatemeh Torabi; Scholman, Merel C.J.. - : Linguistic Data Consortium, 2021. : https://www.ldc.upenn.edu, 2021
Abstract: *Introduction* DiscAlign for Penn and RST Discourse Treebanks was developed by Saarland University. It consists of alignment information for the discourse annotations contained in Penn Discourse Treebank Version 2.0 (LDC2008T05) (PDTB 2.0) and RST Discourse Treebank (LDC2002T07) (RST-DT). PDTB 2.0 and RST-DT annotations overlap for 385 newspaper articles in sections 6, 11, 13, 19 and 23 of the Wall Street Journal corpus contained in Treebank-2 (LDC95T7). DiscAlign for Penn and RST Discourse Treebanks contains approximately 6,700 alignments between PDTB 2.0 and RST-DT relations. DiscAlign for Penn and RST Treebanks is available at no cost to all licensees of PDTB 2.0 and RST-DT and appears in their download queues associated with these corpora as DiscAlign_Penn_RST_DTB_LDC2021T16.zip. *Data* The alignment table is presented as a single UTF-8 encoded CSV file with each row representing a PDTB discourse relation that has been mapped with an RST relation from the RST-DT corpus. Table columns provide some basic information about the source relation extracted from PDTB, the target relation extracted from RST-DT, and the quality of the alignment between the two. See the included documentation for more details on the columns and the mapping procedure. *Samples* Please view this sample (TXT). *Updates* None at this time.
URL: https://catalog.ldc.upenn.edu/LDC2021T16
BASE
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3
DiscAlign for Penn and RST Discourse Treebanks ...
Demberg, Vera; Asr, Fatemeh; Scholman, Merel. - : Linguistic Data Consortium, 2021
BASE
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4
The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media
In: PLoS One (2021)
BASE
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5
The Gender Gap Tracker: Using Natural Language Processing To Measure Gender Bias in Media
BASE
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