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Sparse PPMI graph averaging boosts Random Indexing embeddings

By PulseAugur Editorial · [1 sources] ·

This paper introduces a method for improving Random Indexing (RI) embeddings by averaging them on a sparse Positive Pointwise Mutual Information (PPMI) graph. The technique showed a significant accuracy increase from 19.4% to 30.7% on a fairytale corpus for semantic analogy questions related to Google family categories. However, the method did not outperform neural baselines on other datasets like text8 and SimLex-999, and even reduced accuracy for some other embedding types. AI

IMPACT This research offers a non-gradient method to enhance specific embedding types, though its broader competitiveness with neural models remains limited.

RANK_REASON The item is a research paper detailing a novel method for improving word embeddings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Sparse PPMI graph averaging boosts Random Indexing embeddings

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The item is a research paper detailing a novel method for improving word embeddings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Sparse Mutual Information Graph Averaging for Improving Random Indexing Embeddings

    Sparse word embedding pipelines can avoid dense co-occurrence matrix materialization, dense factorization, and gradient training while still relying on sparse global corpus statistics. This paper studies Random Indexing (RI) vectors refined by weighted averaging on a sparse Posit…

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