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1
Learning Prototypical Functions for Physical Artifacts ...
BASE
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2
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
Yaghoobzadeh, Yadollah; Schütze, Hinrich; Riloff, Ellen. - : Ludwig-Maximilians-Universität München, 2018
BASE
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3
A Probabilistic Annotation Model for Crowdsourcing Coreference
Kruschwitz, Udo; Chamberlain, Jon; Yu, Juntao. - : Association for Computational Linguistics, 2018
BASE
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4
Investigating the role of argumentation in the rhetorical analysis of scientific publications with neural multi-task learning models
Ponzetto, Simone Paolo; Eckert, Kai; Lauscher, Anne. - : Association for Computational Linguistics, 2018
BASE
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5
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018
BASE
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6
The EventStatus Corpus
Huang, Ruihong; Jurafsky, Daniel; Riloff, Ellen. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
BASE
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7
And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue ...
BASE
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8
Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog ...
BASE
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9
The EventStatus Corpus ...
Huang, Ruihong; Jurafsky, Daniel; Riloff, Ellen. - : Linguistic Data Consortium, 2017
BASE
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10
Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue ...
Abstract: The use of irony and sarcasm in social media allows us to study them at scale for the first time. However, their diversity has made it difficult to construct a high-quality corpus of sarcasm in dialogue. Here, we describe the process of creating a large- scale, highly-diverse corpus of online debate forums dialogue, and our novel methods for operationalizing classes of sarcasm in the form of rhetorical questions and hyperbole. We show that we can use lexico-syntactic cues to reliably retrieve sarcastic utterances with high accuracy. To demonstrate the properties and quality of our corpus, we conduct supervised learning experiments with simple features, and show that we achieve both higher precision and F than previous work on sarcasm in debate forums dialogue. We apply a weakly-supervised linguistic pattern learner and qualitatively analyze the linguistic differences in each class. ... : 11 pages, 4 figures, SIGDIAL 2016 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1709.05404
https://arxiv.org/abs/1709.05404
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11
Domain-specific coreference resolution with lexicalized features
Gilbert, Nathan; Riloff, Ellen M.. - : Association for Computational Linguistics, 2014
BASE
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12
Information extraction
In: Handbook of natural language processing (Boca Raton, Fla., 2010), p. 511-532
MPI für Psycholinguistik
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13
Reconcile: A Coreference Resolution Research Platform
BASE
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14
Inducing domain-specific semantic class taggers from (almost) nothing
In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 275-285
BLLDB
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15
Coreference Resolution With Reconcile
In: DTIC (2010)
BASE
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16
Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art
Riloff, Ellen M.; Stoyanov, Veselin; Gilbert, Nathan; Cardie, Claire. - : Association for Computational Linguistics, 2009
BASE
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17
Toward completeness in concept extraction and classification
Riloff, Ellen M.; Hovy, Eduard; Kozareva, Zornitsa. - : Association for Computational Linguistics, 2009
BASE
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18
Corpus-based semantic lexicon induction with web-based corroboration
Igo, Sean P.; Riloff, Ellen M.. - : Association for Computational Linguistics, 2009
BASE
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19
Unified model of phrasal and sentential evidence for information extraction
Riloff, Ellen M.; Patwardhan, Siddharth. - : Association for Computational Linguistics, 2009
BASE
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20
Semantic class learning from the web with hyponym pattern linkage graphs
Riloff, Ellen M.; Kozareva, Zornitsa; Hovy, Eduard. - : Association for Computational Linguistics, 2008
BASE
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