A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Learning Semantic Similarity for Very Short Texts
2015
2015 IEEE International Conference on Data Mining Workshop (ICDMW)
Levering data on social media, such as Twitter and Facebook, requires information retrieval algorithms to become able to relate very short text fragments to each other. Traditional text similarity methods such as tf-idf cosine-similarity, based on word overlap, mostly fail to produce good results in this case, since word overlap is little or non-existent. Recently, distributed word representations, or word embeddings, have been shown to successfully allow words to match on the semantic level.
doi:10.1109/icdmw.2015.86
dblp:conf/icdm/BoomCBDD15
fatcat:zh3ylvcpcjbo3bd3wi37p2xaxm