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Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference
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In: Transactions of the Association for Computational Linguistics, Vol 10, Pp 240-256 (2022) (2022)
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Sentiment Analysis of Short Informal Texts
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In: http://saifmohammad.com/WebDocs/NRC-Sentiment-JAIR-2014.pdf (2014)
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NRC-Canada-2014: Recent improvements in sentiment analysis of tweets, in:
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In: http://www.cs.toronto.edu/%7Exzhu/SemEval2014_NRC_t9.pdf (2014)
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NRC-Canada-2014: Recent improvements in sentiment analysis of tweets, in:
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In: http://saifmohammad.com/WebDocs/SemEval2014-Task9.pdf (2014)
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NRCCanada: Building the State-of-the-Art in Sentiment Analysis of Tweets
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In: http://www.aclweb.org/anthology/S/S13/S13-2053.pdf (2013)
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Prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels
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In: http://aclweb.org/anthology-new/W/W08/W08-0403.pdf (2008)
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Utterance-level extractive summarization of open-domain spontaneous conversations with rich features
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In: http://www.cecs.uci.edu/~papers/icme06/pdfs/0000793.pdf (2006)
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Summarization of spontaneous conversations
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In: http://www.cs.toronto.edu/%7Egpenn/papers/zhu-penn-cscw06.pdf (2006)
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Analysis of polarity information in medical text
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In: http://ftp.cs.toronto.edu/pub/gh/Niu-etal-2005.pdf (2005)
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Analysis of polarity information in medical text
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In: http://www.cs.toronto.edu/~yun/papers/Niu_amia05.pdf (2005)
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Single Character Chinese Named Entity Recognition
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In: http://acl.ldc.upenn.edu/acl2003/sighan/pdf/Zhu.pdf (2003)
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Single Character Chinese Named Entity Recognition
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In: http://acl.ldc.upenn.edu/acl2003/sighan/pdfs/Zhu.pdf (2003)
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Abstract:
Single character named entity (SCNE) is a name entity (NE) composed of one Chinese character, such as " # " (zhong1, China) and " #" # e2,Russia #. SCNE is very common in written Chinese text. However, due to the lack of in-depth research, SCNE is a major source of errors in named entity recognition (NER). This paper formulates the SCNE recognition within the sourcechannel model framework. Our experiments show very encouraging results: an Fscore of 81.01% for single character location name recognition, and an F-score of 68.02% for single character person name recognition. An alternative view of the SCNE recognition problem is to formulate it as a classification task. We construct two classifiers based on maximum entropy model (ME) and vector space model (VSM), respectively. We compare all proposed approaches, showing that the sourcechannel model performs the best in most cases. 1
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.7753 http://acl.ldc.upenn.edu/acl2003/sighan/pdfs/Zhu.pdf
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Sentiment, Emotion, Purpose, and Style in Electoral Tweets
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In: http://saifmohammad.com/WebDocs/tweetSentiment.pdf
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Prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels
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In: http://www.mt-archive.info/ACL-SSST-2008-Zhou.pdf
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Summarizing multiple spoken documents: finding evidence from untranscribed audio
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In: http://aclweb.org/anthology-new/P/P09/P09-1062.pdf
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Ecological Validity and the Evaluation of Speech Summarization Quality
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In: http://www.aclweb.org/anthology/W/W12/W12-2604.pdf
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