1 |
Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference
|
|
|
|
In: Transactions of the Association for Computational Linguistics, Vol 10, Pp 240-256 (2022) (2022)
|
|
BASE
|
|
Show details
|
|
2 |
Sentiment Analysis of Short Informal Texts
|
|
|
|
In: http://saifmohammad.com/WebDocs/NRC-Sentiment-JAIR-2014.pdf (2014)
|
|
BASE
|
|
Show details
|
|
3 |
NRC-Canada-2014: Recent improvements in sentiment analysis of tweets, in:
|
|
|
|
In: http://www.cs.toronto.edu/%7Exzhu/SemEval2014_NRC_t9.pdf (2014)
|
|
BASE
|
|
Show details
|
|
4 |
NRC-Canada-2014: Recent improvements in sentiment analysis of tweets, in:
|
|
|
|
In: http://saifmohammad.com/WebDocs/SemEval2014-Task9.pdf (2014)
|
|
BASE
|
|
Show details
|
|
5 |
NRCCanada: Building the State-of-the-Art in Sentiment Analysis of Tweets
|
|
|
|
In: http://www.aclweb.org/anthology/S/S13/S13-2053.pdf (2013)
|
|
BASE
|
|
Show details
|
|
6 |
Prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels
|
|
|
|
In: http://aclweb.org/anthology-new/W/W08/W08-0403.pdf (2008)
|
|
BASE
|
|
Show details
|
|
7 |
Utterance-level extractive summarization of open-domain spontaneous conversations with rich features
|
|
|
|
In: http://www.cecs.uci.edu/~papers/icme06/pdfs/0000793.pdf (2006)
|
|
BASE
|
|
Show details
|
|
8 |
Summarization of spontaneous conversations
|
|
|
|
In: http://www.cs.toronto.edu/%7Egpenn/papers/zhu-penn-cscw06.pdf (2006)
|
|
BASE
|
|
Show details
|
|
9 |
Analysis of polarity information in medical text
|
|
|
|
In: http://ftp.cs.toronto.edu/pub/gh/Niu-etal-2005.pdf (2005)
|
|
BASE
|
|
Show details
|
|
10 |
Analysis of polarity information in medical text
|
|
|
|
In: http://www.cs.toronto.edu/~yun/papers/Niu_amia05.pdf (2005)
|
|
Abstract:
Knowing the polarity of clinical outcomes is important in answering questions posed by clinicians in patient treatment. We treat analysis of this information as a classification problem. Natural language processing and machine learning techniques are applied to detect four possibilities in medical text: no outcome, positive outcome, negative outcome, and neutral outcome. A supervised learning method is used to perform the classification at the sentence level. Five feature sets are constructed: UNIGRAMS, BIGRAMS, CHANGE PHRASES, NEGATIONS, and CATEGORIES. The performance of different combinations of feature sets is compared. The results show that generalization using the category information in the domain knowledge base Unified Medical Language System is effective in the task. The effect of context information is significant. Combining linguistic features and domain knowledge leads to the highest accuracy.
|
|
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.5848 http://www.cs.toronto.edu/~yun/papers/Niu_amia05.pdf
|
|
BASE
|
|
Hide details
|
|
11 |
Single Character Chinese Named Entity Recognition
|
|
|
|
In: http://acl.ldc.upenn.edu/acl2003/sighan/pdf/Zhu.pdf (2003)
|
|
BASE
|
|
Show details
|
|
12 |
Single Character Chinese Named Entity Recognition
|
|
|
|
In: http://acl.ldc.upenn.edu/acl2003/sighan/pdfs/Zhu.pdf (2003)
|
|
BASE
|
|
Show details
|
|
13 |
Sentiment, Emotion, Purpose, and Style in Electoral Tweets
|
|
|
|
In: http://saifmohammad.com/WebDocs/tweetSentiment.pdf
|
|
BASE
|
|
Show details
|
|
14 |
Prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels
|
|
|
|
In: http://www.mt-archive.info/ACL-SSST-2008-Zhou.pdf
|
|
BASE
|
|
Show details
|
|
15 |
Summarizing multiple spoken documents: finding evidence from untranscribed audio
|
|
|
|
In: http://aclweb.org/anthology-new/P/P09/P09-1062.pdf
|
|
BASE
|
|
Show details
|
|
16 |
Ecological Validity and the Evaluation of Speech Summarization Quality
|
|
|
|
In: http://www.aclweb.org/anthology/W/W12/W12-2604.pdf
|
|
BASE
|
|
Show details
|
|
|
|