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WordNet Affect: an Affective Extension of WordNet
TLDR
A linguistic resource for the lexical representation of affective knowledge was developed starting from WORDNET, through a selection and tagging of a subset of synsets representing the affective. Expand
Corpus-based and Knowledge-based Measures of Text Semantic Similarity
TLDR
This paper shows that the semantic similarity method out-performs methods based on simple lexical matching, resulting in up to 13% error rate reduction with respect to the traditional vector-based similarity metric. Expand
SemEval-2007 Task 14: Affective Text
TLDR
The data set used in the evaluation and the results obtained by the participating systems are described, meant as an exploration of the connection between emotions and lexical semantics. Expand
Learning to identify emotions in text
TLDR
The construction of a large data set annotated for six basic emotions, ANGER, DISGUST, FEAR, JOY, SADNESS and SURPRISE, and several knowledge-based and corpusbased methods for the automatic identification of these emotions in text are proposed. Expand
The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language
TLDR
It is shown that automatic classification is a viable technique to distinguish between truth and falsehood as expressed in language and a method for class-based feature analysis is introduced, which sheds some light on the features that are characteristic for deceptive text. Expand
Making Computers Laugh: Investigations in Automatic Humor Recognition
TLDR
Through experiments performed on very large data sets, it is shown that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over apriori known baselines. Expand
LEARNING TO LAUGH (AUTOMATICALLY): COMPUTATIONAL MODELS FOR HUMOR RECOGNITION
TLDR
Through experiments performed on very large data sets, it is shown that automatic classification techniques can be effectively used to distinguish between humorous and non‐humorous texts, with significant improvements observed over a priori known baselines. Expand
The role of domain information in Word Sense Disambiguation
TLDR
Results obtained at the SENSEVAL-2 initiative confirm that for a significant subset of words domain information can be used to disambiguate with a very high level of precision. Expand
Developing Affective Lexical Resources
TLDR
A linguistic resource for a lexical representation of affective knowledge was developed starting from WORDNET, through the selection and labeling of the synsets representing affective concepts. Expand
Domain Kernels for Word Sense Disambiguation
TLDR
A supervised Word Sense Disambiguation methodology, that exploits kernel methods to model sense distinctions and defines a kernel function, namely the Domain Kernel, that allowed us to plug "external knowledge" into the supervised learning process. Expand
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