Semantic Textual Similarity of Sentences with Emojis
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- Semantic Textual Similarity of Sentences with Emojis
Final published version, 600 KB, PDF document
In this paper, we extend the task of semantic textual similarity to include sentences which contain emojis. Emojis are ubiquitous on social media today, but are often removed in the pre-processing stage of curating datasets for NLP tasks. In this paper, we qualitatively ascertain the amount of semantic information lost by discounting emojis, as well as show a mechanism of accounting for emojis in a semantic task. We create a sentence similarity dataset of 4000 pairs of tweets with emojis, which have been annotated for relatedness. The corpus contains tweets curated based on common topic as well as by replacement of emojis. The latter was done to analyze the difference in semantics associated with different emojis. We aim to provide an understanding of the information lost by removing emojis by providing a qualitative analysis of the dataset. We also aim to present a method of using both emojis and words for downstream NLP tasks beyond sentiment analysis.
Original language | English |
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Title of host publication | The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020 |
Publisher | Association for Computing Machinery |
Publication date | 2020 |
Pages | 426-430 |
ISBN (Electronic) | 9781450370240 |
DOIs | |
Publication status | Published - 2020 |
Event | 29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China Duration: 20 Apr 2020 → 24 Apr 2020 |
Conference
Conference | 29th International World Wide Web Conference, WWW 2020 |
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Land | Taiwan, Province of China |
By | Taipei |
Periode | 20/04/2020 → 24/04/2020 |
Sponsor | Chunghwa Telecom, et al., Microsoft, Quanta Computer, Taiwan Mobile, ZOOM |
- datasets, emoji, sentence similarity
Research areas
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