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Found 2 papers, 1 papers with code
Representation learning for very short texts using weighted word embedding aggregation
Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc.
Learning Semantic Similarity for Very Short Texts
We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching.
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