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In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb "abandon" in "abandon all hope". This is similar to how negation words like "not" can influence polarity. Both shifters and negation are required for high precision sentiment analysis. Lists of negation words are available for many languages, but the only language for which a sizable lexicon of verbal polarity shifters exists is English.
\n\nThis lexicon was created by bootstrapping a sample of annotated verbs with a supervised classifier that uses a set of data- and resource-driven features. We reproduce and adapt this approach to create a German lexicon of verbal polarity shifters. Thereby, we confirm that the approach works for multiple languages. We further improve classification by leveraging cross-lingual information from the English shifter lexicon. Using this improved approach, we bootstrap a large number of German verbal polarity shifters, reducing the annotation effort drastically. The resulting German lexicon of verbal polarity shifters is made publicly available.
\n\nSupplementary Data
\n\nManual curation.
\r\n", "page": "The idea of natural language processing, short NLP, is to process texts and to understand the texts' conveyed meaning.
\r\n\r\nNatural Language Understanding can be seen as a subtask of Natural Language Processing. However, those terms are often used synonymously.
\r\n\r\nhttps://en.wikipedia.org/wiki/Natural_language_processing
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