The answer most technocrats are leaning towards is vector search technology and Retrieval-Augmented Generation (RAG) models that improve AI experiences. These intelligent search systems are fundamentally changing how users discover information, interact with applications, and receive personalized experiences across industries.

Las incrustaciones de palabras, o word embeddings, son un método que consiste en derivar, por medio de algoritmos diversos, representaciones numéricas vectoriales de los términos presentes en un corpus, de acuerdo con sus relaciones contextuales7Michael Gavin et al., «Spaces of Meaning: Conceptual HIstory, Vector Semantics, and Close Reading», Debates in the Digital Humanities 2019, ed. Matthew K. Gold y Lauren F. Klein (Minneapolis London: University of Minnesota Press, 2019), 243-67.. Este método sigue los principios de la semántica distribucional8

Vectors with a small Euclidean distance from one another are located in the same region of a vector space. Vectors with a high cosine similarity are located in the same general direction from the origin.

Recommendations DON'T use shifted PPMI with SVD. DON'T use SVD "correctly", i.e. without eigenvector weighting (performance drops 15 points compared to with eigenvalue weighting with (p = 0.5)). DO use PPMI and SVD with short contexts (window size of (2)). DO use many negative samples with SGNS. DO always use context distribution smoothing (raise unigram distribution to the power of (lpha = 0.75)) for all methods. DO use SGNS as a baseline (robust, fast and cheap to train). DO try adding context vectors in SGNS and GloVe.

The quality of word representations is generally gauged by its ability to encode syntactical information and handle polysemic behavior (or word senses). These properties result in improved semantic word representations. Recent approaches in this area encode such information into its embeddings by leveraging the context. These methods provide deeper networks that calculate word representations as a function of its context.

Traditional word embedding algorithms assign a distinct vector to each word. This makes them unable to account for polysemy. In a recent work, Upadhyay et al. (2017) provided an innovative way to address this deficit. The authors leveraged multilingual parallel data to learn multi-sense word embeddings.

This is very important as training embeddings from scratch requires large amount of time and resource. Mikolov et al. (2013) tried to address this issue by proposing negative sampling which is nothing but frequency-based sampling of negative terms while training the word2vec model.

A general caveat for word embeddings is that they are highly dependent on the applications in which it is used. Labutov and Lipson (2013) proposed task specific embeddings which retrain the word embeddings to align them in the current task space.

One solution to this problem, as explored by Mikolov et al. (2013), is to identify such phrases based on word co-occurrence and train embeddings for them separately. More recent methods have explored directly learning n-gram embeddings from unlabeled data (Johnson and Zhang, 2015).

The word vector is the arrow from the point where all three axes intersect to the end point defined by the coordinates.

arg maxvw;vcP(w;c)2Dlog11+evcvw

p(D= 1jw;c)the probability that(w;c)came from the data, and byp(D= 0jw;c) =1p(D= 1jw;c)the probability that(w;c)didnot.

Loosely speaking, we seek parameter values (thatis, vector representations for both words and con-texts) such that the dot productvwvcassociatedwith “good” word-context pairs is maximized.

In the skip-gram model, each wordw2Wisassociated with a vectorvw2Rdand similarlyeach contextc2Cis represented as a vectorvc2Rd, whereWis the words vocabulary,Cis the contexts vocabulary, anddis the embed-ding dimensionality.

Neural Word Embedding Methods

dimension of embedding vectors strongly dependson applications and uses, and is basically determinedbased on the performance and memory space (orcalculation speed) trade-of