{"type":"ScholarlyArticle","title":"Hashtag2Vec: Learning Hashtag Representation with Relational Hierarchical Embedding Model","authors":[{"name":"Jie Liu","slug":"jie-liu","given":"Jie","family":"Liu","affiliation":"College of Computer and Control Engineering , Nankai University , Tianjin , China."},{"name":"Zhicheng He","slug":"zhicheng-he","given":"Zhicheng","family":"He","affiliation":"College of Computer and Control Engineering , Nankai University , Tianjin , China."},{"name":"Yalou Huang","slug":"yalou-huang","given":"Yalou","family":"Huang","affiliation":"College of Computer and Control Engineering , Nankai University , Tianjin , China."}],"abstract":"Hashtags have always been important elements in many social network platforms and micro-blog services. Semantic understanding of hashtags is a critical and fundamental task for many applications on social networks, such as event analysis, theme discovery, information retrieval, etc. However, this task is challenging due to the sparsity, polysemy, and synonymy of hashtags. In this paper, we investigate the problem of hashtag embedding by combining the short text content with the various heterogeneous relations in social networks. Specifically, we first establish a network with hashtags as its nodes. Hierarchically, each of the hashtag nodes is associated with a set of tweets and each tweet contains a set of words. Then we devise an embedding model, called Hashtag2Vec, which exploits multiple relations of hashtag-hashtag, hashtag-tweet, tweet-word, and word-word relations based on the hierarchical heterogeneous network. In addition to embedding the hashtags, our proposed framework is capable of embedding the short social texts as well. Extensive experiments are conducted on two real-world datasets, and the results demonstrate the effectiveness of the proposed method.","doi":"10.24963/ijcai.2018/480","url":"https://ijcai.org/proceedings/2018/480","pdf":"https://ijcai.org/proceedings/2018/0480.pdf","markdown":"https://ijcai.org/proceedings/2018/480.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/480","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":480,"pages":{"first":3456,"last":3462},"keywords":["Machine Learning Applications: Networks","Natural Language Processing: Embeddings","Machine Learning: Unsupervised Learning","Natural Language Processing: Text Classification"],"videos":[],"references":[{"position":1,"raw":"[Adamopoulos and Todri, 2015] Panagiotis Adamopoulos and Vilma Todri. 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