This paper introduces two novel model architectures for efficiently learning high-quality word representations from large datasets. The models, called Continuous Bag-of-Words (CBOW) and Continuous Skip-gram (Skip-gram), are designed to capture both syntactic and semantic relationships between words. The quality of the word representations is measured in a task where the similarity between words is tested using vector arithmetic, such as vector("King") - vector("Man") + vector("Woman") ≈ vector("Queen"). These models outperform previous techniques in terms of accuracy while requiring significantly less computational resources. The paper compares different model architectures, including feedforward neural networks (NNLM), recurrent neural networks (RNNLM), and log-linear models. It shows that the CBOW and Skip-gram models achieve high accuracy on both syntactic and semantic similarity tasks. The models are trained on large datasets, such as the Google News corpus, which contains over 6 billion words. The results demonstrate that these models can be trained efficiently using distributed computing frameworks like DistBelief, enabling the training of high-dimensional word vectors on massive datasets. The paper also presents a comprehensive test set for evaluating the performance of word representations on various semantic and syntactic similarity tasks. The results show that the Skip-gram model performs well on semantic tasks, while the CBOW model performs better on syntactic tasks. The models are further evaluated on the Microsoft Sentence Completion Challenge, where they achieve state-of-the-art results. The paper concludes that the proposed models provide high-quality word representations that can be used to improve various NLP applications, including machine translation, information retrieval, and question answering. The results suggest that these models can be trained efficiently on large datasets, making them a promising approach for future NLP tasks.This paper introduces two novel model architectures for efficiently learning high-quality word representations from large datasets. The models, called Continuous Bag-of-Words (CBOW) and Continuous Skip-gram (Skip-gram), are designed to capture both syntactic and semantic relationships between words. The quality of the word representations is measured in a task where the similarity between words is tested using vector arithmetic, such as vector("King") - vector("Man") + vector("Woman") ≈ vector("Queen"). These models outperform previous techniques in terms of accuracy while requiring significantly less computational resources. The paper compares different model architectures, including feedforward neural networks (NNLM), recurrent neural networks (RNNLM), and log-linear models. It shows that the CBOW and Skip-gram models achieve high accuracy on both syntactic and semantic similarity tasks. The models are trained on large datasets, such as the Google News corpus, which contains over 6 billion words. The results demonstrate that these models can be trained efficiently using distributed computing frameworks like DistBelief, enabling the training of high-dimensional word vectors on massive datasets. The paper also presents a comprehensive test set for evaluating the performance of word representations on various semantic and syntactic similarity tasks. The results show that the Skip-gram model performs well on semantic tasks, while the CBOW model performs better on syntactic tasks. The models are further evaluated on the Microsoft Sentence Completion Challenge, where they achieve state-of-the-art results. The paper concludes that the proposed models provide high-quality word representations that can be used to improve various NLP applications, including machine translation, information retrieval, and question answering. The results suggest that these models can be trained efficiently on large datasets, making them a promising approach for future NLP tasks.