# GloVe: Global Vectors for Word Representation

@inproceedings{Pennington2014GloVeGV, title={GloVe: Global Vectors for Word Representation}, author={Jeffrey Pennington and Richard Socher and Christopher D. Manning}, booktitle={Conference on Empirical Methods in Natural Language Processing}, year={2014} }

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. [] Key Method Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with…

## 24,745 Citations

### Rehabilitation of Count-Based Models for Word Vector Representations

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A systematic study of the use of the Hellinger distance to extract semantic representations from the word co-occurrence statistics of large text corpora shows that this distance gives good performance on word similarity and analogy tasks, with a proper type and size of context, and a dimensionality reduction based on a stochastic low-rank approximation.

### Modeling Semantic Relatedness using Global Relation Vectors

- Computer ScienceArXiv
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A novel method which directly learns relation vectors from co-occurrence statistics is introduced, and it is shown how relation vectors can be naturally embedded into the resulting vector space.

### Measuring Enrichment Of Word Embeddings With Subword And Dictionary Information

- Computer Science
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Results show that fine-tuning the vectors with semantic information dramatically improves performance inword similarity; conversely, enriching word vectors with subword information increases performance in word analogy tasks, with the hybrid approach finding a solid middle ground.

### Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding

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The underlying ranking interpretation of word contexts is sufficient to match, and sometimes outperform, the performance of popular methods such as Skip-gram, and by using a quadratic kernel, the model can effectively learn word regions, which outperform existing unsupervised models for the task of hypernym detection.

### Analyzing Structures in the Semantic Vector Space: A Framework for Decomposing Word Embeddings

- Computer ScienceArXiv
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A framework for decomposing word embeddings into smaller meaningful units which are called sub-vectors is presented, which opens up a wide range of possibilities analyzing phenomena in vector space semantics, as well as solving concrete NLP problems.

### Word2Box: Learning Word Representation Using Box Embeddings

- Computer ScienceArXiv
- 2021

This model takes a region-based approach to the problem of word representation, representing words as n-dimensional rectangles, and provides additional geometric operations such as intersection and containment which allow them to model co-occurrence patterns vectors struggle with.

### PAWE: Polysemy Aware Word Embeddings

- Computer ScienceICISDM '18
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This work develops a new word embedding model that can accurately represent such words by automatically learning multiple representations for each word, whilst remaining computationally efficient.

### Fast PMI-Based Word Embedding with Efficient Use of Unobserved Patterns

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A new word embedding algorithm that works on a smoothed Positive Pointwise Mutual Information (PPMI) matrix which is obtained from the word-word co-occurrence counts and a kernel similarity measure for the latent space that can effectively calculate the similarities in high dimensions is proposed.

### Distributed Representation of Words in Vector Space for Kannada Language

- Computer Science2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS)
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A distributed representation for Kannada words is proposed using an optimal neural network model and combining various known techniques to improve the vector space representation.

### Learning Word Vectors with Linear Constraints: A Matrix Factorization Approach

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Two new embedding models based on the singular value decomposition of lexical co-occurrences of words are proposed, which allow for injecting linear constraints when performing the decomposition, with which the desired semantic and syntactic information will be maintained in word vectors.

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