A corpus-based analysis of meaning variations in German tag questions Evidence from spoken and written conversational corpora

Clausen, Yulia; Scheffler, Tatjana

doi:10.1515/cllt-2019-0060

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…The polarity dictionary published by Taiwan University has high accuracy, including 2810 positive words and 8276 negative words. Corpora of words labelled with emotional polarities could also be found for the Arabic [14,15], German [16], or Russian [17] languages.…”

Section: Progress Of Emotion Corpora Constructionmentioning

confidence: 99%

Developing Multi-Labelled Corpus of Twitter Short Texts: A Semi-Automatic Method

Liu,

Zhou,

Kong

et al. 2023

Systems

140

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Facing fast-increasing electronic documents in the Digital Media Age, the need to extract textual features of online texts for better communication is growing. Sentiment classification might be the key method to catch emotions of online communication, and developing corpora with annotation of emotions is the first step to achieving sentiment classification. However, the labour-intensive and costly manual annotation has resulted in the lack of corpora for emotional words. Furthermore, single-label semantic corpora could hardly meet the requirement of modern analysis of complicated user’s emotions, but tagging emotional words with multiple labels is even more difficult than usual. Improvement of the methods of automatic emotion tagging with multiple emotion labels to construct new semantic corpora is urgently needed. Taking Twitter short texts as the case, this study proposes a new semi-automatic method to annotate Internet short texts with multiple labels and form a multi-labelled corpus for further algorithm training. Each sentence is tagged with both the emotional tendency and polarity, and each tweet, which generally contains several sentences, is tagged with the first two major emotional tendencies. The semi-automatic multi-labelled annotation is achieved through the process of selecting the base corpus and emotional tags, data preprocessing, automatic annotation through word matching and weight calculation, and manual correction in case of multiple emotional tendencies are found. The experiments on the Sentiment140 published Twitter corpus demonstrate the effectiveness of the proposed approach and show consistency between the results of semi-automatic annotation and manual annotation. By applying this method, this study summarises the annotation specification and constructs a multi-labelled emotion corpus with 6500 tweets for further algorithm training.

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Section: Progress Of Emotion Corpora Constructionmentioning

confidence: 99%

Developing Multi-Labelled Corpus of Twitter Short Texts: A Semi-Automatic Method

Liu,

Zhou,

Kong

et al. 2023

Systems

140

View full text Add to dashboard Cite

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“Won’t you?” reverse-polarity question tags in American English as a window into the semantics-pragmatics interface

Scheffler

Malamud

2023

Linguist and Philos

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We model the conventional meaning of utterances that combine two distinct clause types: a (positive) declarative or imperative (in rare cases, interrogative) anchor and a (negative) interrogative tag, such as won’t you?. We argue that such utterances express a single speech act, and in fact, a single conventional update of the conversational scoreboard. The proposed model of this effect is a straightforward extension of prior proposals for the semantics of declaratives, imperatives, and preposed-negation interrogatives. Ours is the first unified account of these phenomena that addresses the sentential force of these utterances and outlines how the speech act effects arise from the scoreboard update and contextual factors. We enrich the conversational scoreboard, interpreted as a model of sentential force, to include graded commitments and non-at-issue meanings. A consequence of our model is that modified utterances can create “blended” speech acts which share some, but not all, properties with the unmodified utterances. The proposal has implications for models of other utterance modifiers, as well as for negative interrogatives and negation in general, and for imperative/jussive constructions.

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Joint attention and reference construction: The role of pointing and “so”

Balantani

Lázaro

2021

Language & Communication

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A corpus-based analysis of meaning variations in German tag questions Evidence from spoken and written conversational corpora

Cited by 4 publications

References 27 publications

Developing Multi-Labelled Corpus of Twitter Short Texts: A Semi-Automatic Method

Developing Multi-Labelled Corpus of Twitter Short Texts: A Semi-Automatic Method

“Won’t you?” reverse-polarity question tags in American English as a window into the semantics-pragmatics interface

Joint attention and reference construction: The role of pointing and “so”

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