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The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.77 Abstract: Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25. However, no previous work investigated how dense representations perform with large index sizes. We show theoretically and empirically that the performance for dense representations decreases quicker than sparse representations for increasing index sizes. In extreme cases, this can even lead to a tipping point where at a certain index size sparse representations outperform dense representations. We show that this behavior is tightly connected to the number of dimensions of the representations: The lower the dimension, the higher the chance for false positives, i.e. returning irrelevant documents ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/z3am-4347
https://underline.io/lecture/25657-the-curse-of-dense-low-dimensional-information-retrieval-for-large-index-sizes
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2
Green NLP panel ...
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3
Investigating label suggestions for opinion mining in German Covid-19 social media ...
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4
Coreference Reasoning in Machine Reading Comprehension ...
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5
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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6
Metaphor Generation with Conceptual Mappings ...
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