44 citations · 52 across the 7 of their papers we have counts for
5 papers · 1 filter
TweetNERD -- End to End Entity Linking Benchmark for Tweets
Shubhanshu Mishra, Aman Saini, Raheleh Makki +3
Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (N…
CTM -- A Model for Large-Scale Multi-View Tweet Topic Classification
Vivek Kulkarni, Kenny Leung, Aria Haghighi
Automatically associating social media posts with topics is an important prerequisite for effective search and recommendation on many social media platforms. However, topic classif…
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
Ahmed El-Kishky, Thomas Markovich, Kenny Leung +3
Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate item…
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
Ahmed El-Kishky, Thomas Markovich, Serim Park +8
Social networks, such as Twitter, form a heterogeneous information network (HIN) where nodes represent domain entities (e.g., user, content, advertiser, etc.) and edges represent o…
Learning Stance Embeddings from Signed Social Graphs
John Pougué-Biyong, Akshay Gupta, Aria Haghighi +1
A key challenge in social network analysis is understanding the position, or stance, of people in the graph on a large set of topics. While past work has modeled (dis)agreement in…