DE eng

Search in the Catalogues and Directories

Hits 1 – 16 of 16

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

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
16
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern