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Found 7 papers, 3 papers with code
CTM - A Model for Large-Scale Multi-View Tweet Topic Classification
In contrast to most prior work which only focuses on post-classification into a small number of topics (10-20), we consider the task of large-scale topic classification in the context of Twitter where the topic space is 10 times larger with potentially multiple topic associations per Tweet.
TweetNERD -- End to End Entity Linking Benchmark for Tweets
Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (NLP) applications.
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
To this end, we introduce kNN-Embed, a general approach to improving diversity in dense ANN-based retrieval.
CTM -- A Model for Large-Scale Multi-View Tweet Topic Classification
In contrast to most prior work which only focuses on post classification into a small number of topics ($10$-$20$), we consider the task of large-scale topic classification in the context of Twitter where the topic space is $10$ times larger with potentially multiple topic associations per Tweet.
Learning Stance Embeddings from Signed Social Graphs
We propose the Stance Embeddings Model(SEM), which jointly learns embeddings for each user and topic in signed social graphs with distinct edge types for each topic.
LMSOC: An Approach for Socially Sensitive Pretraining
Although language depends heavily on the geographical, temporal, and other social contexts of the speaker, these elements have not been incorporated into modern transformer-based language models.
Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair Prediction
We evaluate a simple approach to improving zero-shot multilingual transfer of mBERT on social media corpus by adding a pretraining task called translation pair prediction (TPP), which predicts whether a pair of cross-lingual texts are a valid translation.
