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Peter Potash
Peter Potash
Microsoft Turing Montreal
Verified email at microsoft.com
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Year
Ghostwriter: Using an lstm for automatic rap lyric generation
P Potash, A Romanov, A Rumshisky
Proceedings of the 2015 Conference on Empirical Methods in Natural Language …, 2015
1042015
Semeval-2017 task 6:# hashtagwars: Learning a sense of humor
P Potash, A Romanov, A Rumshisky
Proceedings of the 11th International Workshop on Semantic Evaluation …, 2017
622017
Here's my point: Joint pointer architecture for argument mining
P Potash, A Romanov, A Rumshisky
arXiv preprint arXiv:1612.08994, 2016
612016
Towards debate automation: a recurrent model for predicting debate winners
P Potash, A Rumshisky
Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017
242017
Twitterhawk: A feature bucket based approach to sentiment analysis
W Boag, P Potash, A Rumshisky
Proceedings of the 9th International Workshop on Semantic Evaluation …, 2015
232015
Operationalizing the legal principle of data minimization for personalization
AJ Biega, P Potash, H Daumé, F Diaz, M Finck
Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020
182020
Length, interchangeability, and external knowledge: Observations from predicting argument convincingness
P Potash, R Bhattacharya, A Rumshisky
Proceedings of the Eighth International Joint Conference on Natural Language …, 2017
122017
Combining network and language indicators for tracking conflict intensity
A Rumshisky, M Gronas, P Potash, M Dubov, A Romanov, S Kulshreshtha, ...
International Conference on Social Informatics, 391-404, 2017
102017
Evaluating creative language generation: The case of rap lyric ghostwriting
P Potash, A Romanov, A Rumshisky
arXiv preprint arXiv:1612.03205, 2016
82016
Ranking passages for argument convincingness
P Potash, A Ferguson, TJ Hazen
Proceedings of the 6th Workshop on Argument Mining, 146-155, 2019
72019
# HashtagWars: Learning a Sense of Humor
P Potash, A Romanov, A Rumshisky
arXiv preprint arXiv:1612.03216, 2016
72016
Using topic modeling and text embeddings to predict deleted tweets
PJ Potash, EB Bell, JJ Harrison
Pacific Northwest National Lab.(PNNL), Richland, WA (United States), 2016
62016
Simihawk at semeval-2016 task 1: A deep ensemble system for semantic textual similarity
P Potash, W Boag, A Romanov, V Ramanishka, A Rumshisky
Proceedings of the 10th International Workshop on Semantic Evaluation …, 2016
52016
Recommender System Incorporating User Personality Profile through Analysis of Written Reviews.
P Potash, A Rumshisky
EMPIRE@ RecSys, 60-66, 2016
42016
Here's My Point: Argumentation Mining with Pointer Networks
P Potash, A Romanov, A Rumshisky
32016
Predictive model for ranking argument convincingness of text passages
P Potash, TJ Hazen
US Patent App. 16/785,359, 2021
12021
Playing log (n)-questions over sentences
P Potash, K Suleman
arXiv preprint arXiv:1908.04660, 2019
12019
Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013-2014.
P Potash, A Romanov, A Rumshisky, M Gronas
NLPmJ@ EMNLP, 13-18, 2017
12017
Neural argumentation: Structure and persuasion
P Potash
University of Massachusetts Lowell, 2017
12017
The Effect of Downstream Classification Tasks for Evaluating Sentence Embeddings
P Potash
arXiv preprint arXiv:1904.02228, 2019
2019
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