Papers › All-but-the-Top: Simple and Effective Postprocessing for Word Representations
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
Jiaqi Mu, Suma Bhat, Pramod Viswanath
Real-valued word representations have transformed NLP applications; popular examples are word2vec and GloVe, recognized for their ability to capture linguistic regularities. In this paper, we demonstrate a {\em very simple}, and yet counter-intuitive, postprocessing technique -- eliminate the common mean vector and a few top dominating directions from the word vectors -- that renders off-the-shelf representations {\em even stronger}. The postprocessing is empirically validated on a variety of lexical-level intrinsic tasks (word similarity, concept categorization, word analogy) and sentence-level tasks (semantic textural similarity and { text classification}) on multiple datasets and with a variety of representation methods and hyperparameter choices in multiple languages; in each case, the processed representations are consistently better than the original ones.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | MR | GRU-RNN-WORD2VEC | Accuracy | 78.26 | #11 of 19 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | GRU-RNN-WORD2VEC | Accuracy | 45.02 | #25 of 31 | Archive leaderboard | report |
| Subjectivity Analysis | SUBJ | GRU-RNN-GLOVE | Accuracy | 91.85 | #15 of 19 | Archive leaderboard | report |
| Text Classification | TREC-6 | GRU-RNN-GLOVE | Error | 7.0 | #13 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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