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        <title>Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German</title>
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        <namePart type="given">Marc</namePart>
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        <namePart type="given">Michael</namePart>
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    <name type="personal">
        <namePart type="given">Josef</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>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. This 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.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German
%A Schulder, Marc
%A Wiegand, Michael
%A Ruppenhofer, Josef
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F schulder-etal-2018-automatically
%X 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. This 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.
%U https://aclanthology.org/C18-1213
%P 2516-2528

Markdown (Informal)

[Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German](https://aclanthology.org/C18-1213) (Schulder et al., COLING 2018)

• Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German (Schulder et al., COLING 2018)

ACL

• Marc Schulder, Michael Wiegand, and Josef Ruppenhofer. 2018. Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2516–2528, Santa Fe, New Mexico, USA. Association for Computational Linguistics.