Separating Actor-View from Speaker-View Opinion Expressions using Linguistic Features

Wiegand, Michael; Schulder, Marc; Ruppenhofer, Josef

doi:10.18653/v1/n16-1092

Cited by 5 publications

(37 citation statements)

References 21 publications

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“…Some work focuses entirely on labeling of opinion expressions (Yang and Cardie, 2014;Irsoy and Cardie, 2014). Other work looks into specific subcategories of ORL: opinion role induction for verbal predicates (Wiegand and Ruppenhofer, 2015), categorization of opinion words into actor and speaker view (Wiegand et al, 2016b), opinion roles extraction on opinion compounds (Wiegand et al, 2016a). Wiegand and Ruppenhofer (2015) report 72.54 binary F1 score for labeling of holders in MPQA (results for targets are not reported).…”

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confidence: 99%

SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling

Marasović¹,

Frank²

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question Who expressed what kind of sentiment towards what?. Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). We suspect this is due to the scarcity of labeled training data and address this issue using different multi-task learning (MTL) techniques with a related task which has substantially more data, i.e. Semantic Role Labeling (SRL). We show that two MTL models improve significantly over the single-task model for labeling of both holders and targets, on the development and the test sets. We found that the vanilla MTL model which makes predictions using only shared ORL and SRL features, performs the best. With deeper analysis we determine what works and what might be done to make further improvements for ORL.

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confidence: 99%

SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling

Marasović¹,

Frank²

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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“…Such comparisons are rarely perceived as abusive. This distinction bears a resemblance to the distinction of sentiment views proposed by Wiegand et al (2016). That work proposes a binary distinction into speaker views, which resembles evaluative comparisons, and actor views, which resembles descriptions of the emotional frame of mind.…”

Section: Manually Designed Featuresmentioning

confidence: 62%

“…Wiegand et al ( 2016) also provide a list of verbs, nouns and adjectives classified into either of the two categories. Due to the fact that this lexicon seems inaccurate when it comes to ambiguous words 7 , we annotated the binary distinction of evaluation vs. emotional frame of mind manually in addition to using the resource from Wiegand et al (2016).…”

Section: Manually Designed Featuresmentioning

confidence: 99%

Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis

Wiegand

Geulig²,

Ruppenhofer

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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We examine the task of detecting implicitly abusive comparisons (e.g. Your hair looks like you have been electrocuted). Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g. dumbass or scum) are absent. We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons. We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons.

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“…The works most closely related to ours are Wiegand et al (2016), Deng and Wiebe (2016), and Wiegand and Ruppenhofer (2015) who all successfully distinguish sentiment views on the lexeme level out of context: Wiegand et al (2016) take into account the opinion adjectives, nouns, and verbs from the Subjectivity Lexicon (Wilson et al 2005). As a gold standard, these words are manually annotated with sentiment-view information.…”

Section: Previous Work On Sentiment Viewsmentioning

confidence: 99%

“…In principle, every corpus-based method can be implemented with the help of either of those corpora. Following Wiegand et al (2016), the first corpus we use is NEWS-the North American News Text Corpus (LDC95T21). Although it is the smallest corpus, it has the advantage of comprising well-written text.…”

Section: Corporamentioning

confidence: 99%

Determining sentiment views of verbal multiword expressions using linguistic features

2023

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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.

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Separating Actor-View from Speaker-View Opinion Expressions using Linguistic Features

Cited by 5 publications

References 21 publications

SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling

SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling

Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis

Determining sentiment views of verbal multiword expressions using linguistic features

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