Abstract | ||
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Reflecting the rapid growth in the use of Social Networking Services (SNSs), it has of late become popular for users to share their feelings, impression, and opinions with each other, about what they saw or experienced, rapidly by means of short text messages (SMS). This trend has let a large number of users consciously or unconsciously use emotion-bearing words and also acronyms to reduce the number of characters to type. We have noticed this new emerging category of language unit, namely "Emotion-Driven Acronyms (EDAs)". Because by definition, each acronym consists of less characters than its original full form, the acronyms for different full forms often coincidently identical. Consequently, the misuse of EDAs substantially decreases the readability of messages. Our long-term research goal is to normalize text in a corrupt language into the canonical one. In this paper, as the first step towards the exploration of EDAs, we focus only on the normalization for EDAs and propose a method to disambiguate the occurrence of an EDA that corresponds to different full forms depending on the context, such as "smh (so much hate / shaking my head)". We also demonstrate what kind of features are effective in our task experimentally and discuss the nature of EDAs from different perspectives. |
Year | DOI | Venue |
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2019 | 10.5220/0007407707310738 | PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE (ICAART), VOL 2 |
Keywords | Field | DocType |
Natural Language Processing, Word Sense Disambiguation, Text Normalization, Social Networking Service, Information Retrieval, Acronym, Emotion | Computer science,Artificial intelligence,Natural language processing,Decoding methods,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
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Bizhanova Aizhan | 1 | 0 | 0.34 |
Atsushi Fujii | 2 | 486 | 59.25 |