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1
Linguistic models for analyzing and detecting biased language
In: http://www.mpi-sws.org/~cristian/Biased_language_files/neutrality.pdf (2013)
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2
Linguistic models for analyzing and detecting biased language
In: http://aclweb.org/anthology/P/P13/P13-1162.pdf (2013)
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3
Semeval-2010 task 1: Coreference resolution in multiple languages
In: http://aclweb.org/anthology-new/S/S10/S10-1001.pdf (2010)
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4
A deeper look into features for coreference resolution
In: http://www.isi.edu/natural-language/people/hovy/papers/09DAARC-coref-final.pdf (2009)
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5
Ancora: Multi level annotated corpora for Catalan and
In: http://www.lrec-conf.org/proceedings/lrec2008/pdf/35_paper.pdf (2008)
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6
Discourse deixis and coreference: evidence from AnCora
In: http://dspace.utlib.ee/dspace/bitstream/handle/10062/7367/Recasens.pdf;jsessionid=5578823987039FA516B852AD9BEF02F4?sequence=1 (2008)
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7
Where anaphora and coreference meet. Annotation in the Spanish CESS-ECE corpus
In: http://clic.ub.edu/sites/default/files/users/RANLP07.pdf (2007)
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8
A Chain-starting Classifier of Definite NPs in Spanish
In: http://aclweb.org/anthology-new/E/E09/E09-3006.pdf
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9
The Life and Death of Discourse Entities: Identifying Singleton Mentions
In: http://nlp.stanford.edu/pubs/discourse-referent-lifespans.pdf
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10
The Life and Death of Discourse Entities: Identifying Singleton Mentions
In: http://www.aclweb.org/anthology-new/N/N13/N13-1071.pdf
Abstract: A discourse typically involves numerous entities, but few are mentioned more than once. Distinguishing discourse entities that die out after just one mention (singletons) from those that lead longer lives (coreferent) would benefit NLP applications such as coreference resolution, protagonist identification, topic modeling, and discourse coherence. We build a logistic regression model for predicting the singleton/coreferent distinction, drawing on linguistic insights about how discourse entity lifespans are affected by syntactic and semantic features. The model is effective in its own right (78 % accuracy), and incorporating it into a state-of-the-art coreference resolution system yields a significant improvement. 1
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.8097
http://www.aclweb.org/anthology-new/N/N13/N13-1071.pdf
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11
A Typology of Near-Identity Relations for Coreference (NIDENT)
In: http://www.lrec-conf.org/proceedings/lrec2010/pdf/160_Paper.pdf
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12
Coreference Resolution: An Empirical Study Based on SemEval-2010 Shared Task 1
In: http://nlp.stanford.edu/pubs/MarquezRecasensSapena12.pdf
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13
Annotating Near-Identity from Coreference Disagreements
In: http://nlp.stanford.edu/pubs/RecasensMartiOrasan12.pdf
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14
Squibs On Paraphrase and Coreference
In: http://wing.comp.nus.edu.sg/~antho/J/J10/J10-4004.pdf
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15
Linguistic Models for Analyzing and Detecting Biased Language
In: http://www.stanford.edu/~jurafsky/neutrality.pdf
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16
Linguistic Models for Analyzing and Detecting Biased Language
In: http://nlp.stanford.edu/pubs/neutrality.pdf
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17
SemEval-2010 Task 1: coreference resolution in multiple languages
In: http://nlp.lsi.upc.edu/papers/semeval2-task1.pdf
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