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Titel: Determining sentiment views of verbal multiword expressions using linguistic features
VerfasserIn: Wiegand, Michael
Schulder, Marc
Ruppenhofer, Josef
Sprache: Englisch
Titel: Natural language engineering
Bandnummer: 30
Heft: 2
Seiten: 256-293
Verlag/Plattform: Cambridge University Press
Erscheinungsjahr: 2023
Freie Schlagwörter: Sentiment analysis
Opinion mining
Lexical semantics
Opinion holder extraction
Multiword expressions
DDC-Sachgruppe: 400 Sprache, Linguistik
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: We examine the binary classification of sentiment views for verbal multiword expressions (MWEs). Sentiment views denote the perspective of the holder of some opinion. We distinguish between MWEs conveying the view of the speaker of the utterance (e.g., in “The company reinvented the wheel” the holder is the implicit speaker who criticizes the company for creating something already existing) and MWEs conveying the view of explicit entities participating in an opinion event (e.g., in “Peter threw in the towel” the holder is Peter having given up something). The task has so far been examined on unigram opinion words. Since many features found effective for unigrams are not usable for MWEs, we propose novel ones taking into account the internal structure of MWEs, a unigram sentiment-view lexicon and various information from Wiktionary. We also examine distributional methods and show that the corpus on which a representation is induced has a notable impact on the classification. We perform an extrinsic evaluation in the task of opinion holder extraction and show that the learnt knowledge also improves a state-of-the-art classifier trained on BERT. Sentiment-view classification is typically framed as a task in which only little labeled training data are available. As in the case of unigrams, we show that for MWEs a feature-based approach beats state-of-the-art generic methods.
DOI der Erstveröffentlichung: 10.1017/S1351324923000153
URL der Erstveröffentlichung: https://www.cambridge.org/core/journals/natural-language-engineering/article/determining-sentiment-views-of-verbal-multiword-expressions-using-linguistic-features/B992222E564C948CE90EA7238C0E9195
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-437280
hdl:20.500.11880/39161
http://dx.doi.org/10.22028/D291-43728
ISSN: 1469-8110
1351-3249
Datum des Eintrags: 11-Dez-2024
Fakultät: P - Philosophische Fakultät
Fachrichtung: P - Sprachwissenschaft und Sprachtechnologie
Professur: P - Keiner Professur zugeordnet
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons