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Lexical Representation of Dense Numerical Vectors: Introducing LangVec

„Аз-буки“ by „Аз-буки“
02-07-2024
in Uncategorized
A A

Simeon Emanuilov, Aleksandar Dimov
Sofia University “St. Kliment Ohridski” (Bulgaria)

https://doi.org/10.53656/math2024-3-1-lex

Abstract. High-dimensional numerical vectors are widely used in machine learning for searching and indexing data. However, it is often difficult for users to interpret their meaning. To address this, we introduce a novel approach that transforms dense vectors into human-readable lexical representations using a percentile-based mapping approach. The essence of the approach is a mapping of words from a predefined/custom lexicon to vectors based on their relative local magnitudes. This way, it enables intuitive visualization of the semantic similarities and differences between complex data points and allows for domain-specific interpretability. It provides an easy way to deduplicate dense vectors (even near-duplicates) and can generate locality-aware hash-like representations, which can be used for efficient indexing and retrieval in various applications. The approach has also been implemented in an open-source library called LangVec. The paper provides examples on LangVec usage and highlights the key applications, including semantic search, recommendation systems, and clustering of numerical data into a human-readable format.
Keywords: interpretable machine learning, vector representations, lexical mapping, semantic similarity, clustering, recommendation systems

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Свързани статии:

Default ThumbnailThe Philosophical Foundations of Pedagogy of Informal Education Default ThumbnailApplication of Project-based Learning in the Literature Lessons (1st – 4th grade) Default ThumbnailA Monograph on the Crimean War (1853 – 1856) and the Bulgarian Lands, Presented through an Original French Source from the Age Default ThumbnailBulgarian Information Consortium. Twenty Years of Library Cooperation
Tags clusteringinterpretable machine learninglexical mappingrecommendation systemssemantic similarityvector representations

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