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Sarcasm as Contrast between a Positive Sentiment and Negative Situation
TLDR
This work develops a sarcasm recognizer that automatically learns lists of positive sentiment phrases and negative situation phrases from sarcastic tweets and shows that identifying contrasting contexts using the phrases learned through bootstrapping yields improved recall for sarcasm recognition. Expand
Learning Extraction Patterns for Subjective Expressions
TLDR
A bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions while maintaining high precision is presented. Expand
Automatically Generating Extraction Patterns from Untagged Text
  • E. Riloff
  • Computer Science
  • AAAI/IAAI, Vol. 2
  • 4 August 1996
TLDR
This work has developed a system called AutoSlog-TS that creates dictionaries of extraction patterns using only untagged text, and in experiments with the MUG-4 terrorism domain, created a dictionary of extraction pattern that performed comparably to a dictionary created by autoSlog, using only preclassified texts as input. Expand
OpinionFinder: A System for Subjectivity Analysis
OpinionFinder is a system that performs subjectivity analysis, automatically identifying when opinions, sentiments, speculations, and other private states are present in text. Specifically,Expand
A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts
TLDR
The semantic lexicons produced by Basilisk have higher precision than those produced by previous techniques, with several categories showing substantial improvement. Expand
Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping
TLDR
A multilevel bootstrapping algorithm is presented that generates both the semantic lexicon and extraction patterns simultaneously simultaneously and produces high-quality dictionaries for several semantic categories. Expand
Creating Subjective and Objective Sentence Classifiers from Unannotated Texts
TLDR
The results of developing subjectivity classifiers using only unannotated texts for training rivals that of previous supervised learning approaches and advances the state of the art in objective sentence classification. Expand
Learning subjective nouns using extraction pattern bootstrapping
TLDR
The goal of the research is to develop a system that can distinguish subjective sentences from objective sentences, and a Naive Bayes classifier is trained using the subjective nouns, discourse features, and subjectivity clues identified in prior research. Expand
Automatically Constructing a Dictionary for Information Extraction Tasks
TLDR
Using AutoSlog, a system that automatically builds a domain-specific dictionary of concepts for extracting information from text, a dictionary for the domain of terrorist event descriptions was constructed in only 5 person-hours and the overall scores were virtually indistinguishable. Expand
Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs
We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associatedExpand
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