Challenge: a large number of verbal polarity shifters are available for multiple languages, but only English has a sizable lexicon of them.
Approach: They use methods to create large lexicon of verbal polarity shifters in germany . they bootstrap annotated verbs with a supervised classifier and apply them to German .
Outcome: The proposed method is able to create a large lexicon of verbal polarity shifters in germany . it reduces annotation effort by leveraging cross-lingual information from the English lexico .

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Introducing a Lexicon of Verbal Polarity Shifters for English (L18-1)

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Challenge: Negation words can change the sentiment polarity of a phrase, but there are more than 1200 other polarities.
Approach: They propose a lexicon of verbal polarity shifters that covers the entirety of verbs found in WordNet.
Outcome: The proposed lexicon covers the entirety of verbs found in WordNet.
Enhancing a Lexicon of Polarity Shifters through the Supervised Classification of Shifting Directions (2020.lrec-1)

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Challenge: Existing polarity shifter lexica only specify when a word can cause shifting, but do not specify when this is limited to a single shifting direction.
Approach: They propose a classifier that determines the shifting direction of polarity shifters by using resource-driven features and data-driven feature.
Outcome: The proposed classifier enhances the largest available polarity shifter lexicon.
Disambiguation of Verbal Shifters (L18-1)

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Challenge: Negation is a contextual phenomenon that needs to be addressed in sentiment analysis.
Approach: They propose a supervised learning approach to disambiguate verbal shifters using generalization features and a new lexicon.
Outcome: The proposed approach takes into account various features, particularly generalization features.
Generating a Gold Standard for a Swedish Sentiment Lexicon (L18-1)

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Challenge: Existing sentiment lexicons are compiled by (machine) translation from English resources, obscuring language-specific characteristics of sentiment-loaded vocabulary.
Approach: They propose a gold standard for sentiment annotation of Swedish terms using the SALDO lexicon and the Gigaword corpus.
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Classifier-based Polarity Propagation in a WordNet (L18-1)

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Challenge: a wordnet-based sentiment lexicon can be built to express sentiment polarity in a way shared across domains.
Approach: They propose a method to build a sense-level sentiment lexicon on the basis of a wordnet . they use a rich set of wordnet-based features to recognize and assign sentiment polarity values .
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Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon (2021.findings-acl)

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Challenge: Existing studies on sentiment lexicons have focused on domain-dependent sentiment words.
Approach: They propose a graph-based technique to detect and correct domain-dependent sentiment words . they propose to use a sentiment lexicon to classify sentiments in a lexical-based classifier .
Outcome: The proposed method is effective on multiple datasets from different domains.
Transformers in the loop: Polarity in neural models of language (2022.acl-long)

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Challenge: Recent Transformer-based language representation models (LRMs) show impressive results on practical text analysis tasks, but do they have access to complex linguistic notions?
Approach: They propose to use polarity as a case study to compare metrics derived from language models to human judgments obtained in psycholinguistic experiments.
Outcome: The proposed model is more accurate than linguistic theory predictions for polarity, and allows us to use language models to discover new insights into natural language grammar beyond existing linguistic theories.
A Thesaurus-based Sentiment Lexicon for Danish: The Danish Sentiment Lexicon (2022.lrec-1)

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Challenge: a newly published Danish sentiment lexicon with a high lexical coverage was compiled using lexicographic methods and linked data.
Approach: They propose to use lexicographic methods to compile a Danish sentiment lexicon with a high lexical coverage by linking words from a thesaurus to a comprehensive monolingual dictionary.
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MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool (P19-3)

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Challenge: In this paper, we demonstrate the online demo and command line interface of a lexical normalization system (MoNoise) for a variety of languages.
Approach: They propose to bundle seven datasets in six languages to form a new benchmark and a novel evaluation metric which is particularly suitable for cross-dataset comparisons.
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Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons (L18-1)

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Challenge: Existing representational frameworks for emotion encoding are incompatible with semantic polarity, resulting in a large amount of incompatible emotion lexicons.
Approach: They propose to map different emotion representation formats onto each other for mutual compatibility and interoperability of language resources.
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