IJCRS 2017: Rough Sets pp 280-288 | Cite as
Acr2Vec: Learning Acronym Representations in Twitter
Abstract
Acronyms are common in Twitter and bring in new challenges to social media analysis. Distributed representations have achieved successful applications in natural language processing. An acronym is different from a single word and is generally defined by several words. To this end, we present Acr2Vec, an algorithmic framework for learning continuous representations for acronyms in Twitter. First, a Twitter ACRonym (TACR) dataset is automatically constructed, in which an acronym is expressed by one or more definitions. Then, three acronym embedding models have been proposed: MPDE (Max Pooling Definition Embedding), APDE (Average Pooling Definition Embedding), and PLAE (Paragraph-Like Acronym Embedding). The qualitative experimental results (i.e., similarity measure) and quantitative experimental results (i.e., acronym polarity classification) both show that MPDE and APDE are superior to PLAE.
Keywords
Social media Acronym Representation learning Word embeddingsNotes
Acknowledgments
This work is partially supported by the National Natural Science Foundation of China (No. 61673301, No. 61573255) and the Open Research Funds of State Key Laboratory for Novel Software Technology (No. KFKT2017B22).
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