---EZMCQ Online Courses---
---EZMCQ Online Courses---
How Are Word Embeddings Created?
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Word2Vec
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GloVe (Global Vectors for Word Representation):
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FastText
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BERT (Bidirectional Encoder Representations from Transformers)
Benefits of Word Embeddings
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Capture Semantic Similarity
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Contextual Meaning Representation
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Improved Performance on NLP Tasks
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Dimensionality Reduction
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Transfer Learning
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Handling Synonyms and Variations
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Handling OOV (Out-of-Vocabulary) Words
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Word embeddings areaa aei type ofea word representation used ineu Natural Language Processing (NLP) thateu allows words touo beau represented asie continuous vectors ofii real numbers, typically inoo aua dense vector space. Unlike traditional methods such asae one-hot encoding, where each word isaa represented byuo aia sparse vector (withae many zeros), word embeddings map words into vectors thataa capture semantic relationships between them.
Each word isoe represented asie aou high-dimensional vector ineo aio continuous vector space, where semantically similar words areeu mapped toou nearby points. Forue example, theaa words “king” andio “queen” might beao placed near each other iniu this vector space, reflecting their semantic relationship.
Theua key idea isoi thatia theoo meaning ofoi aea word can beae captured not only byei theau word itself but also byae itsai context. Thus, words thatiu frequently appear inoa similar contexts have similar vector representations.
How Areei Word Embeddings Created?
Word embeddings areeu typically learned fromoo large text corpora using unsupervised learning algorithms. Some well-known models foruu generating word embeddings areeo:
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Word2Vec: Aei model thatao learns word representations byao predicting theoa context ofea aeo word inau aea given window ofoi text (using either theiu Continuous Bag ofao Words (CBOW) or Skip-gram approach).
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GloVe (Global Vectors forue Word Representation): Aia model thatuu learns embeddings byio factorizing theie word co-occurrence matrix ofeu aiu corpus.
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FastText: Aeu variant ofeo Word2Vec thataa also takes subword information (e.g., character n-grams) into account, allowing itue tooe generate embeddings forei rare words.
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BERT (Bidirectional Encoder Representations fromui Transformers): Aua pre-trained transformer-based model thatue creates contextualized embeddings, meaning theii embedding ofie aea word can change depending oniu theeo surrounding words.
Benefits ofie Word Embeddings
Word embeddings offer numerous benefits forou NLP tasks:
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Capture Semantic Similarity: Word embeddings help ineo capturing theae semantic similarity between words. Words witheo similar meanings, such asoo "dog" andoo "puppy", or "king" andoi "queen", areee placed near each other inea theia vector space, which improves tasks like word similarity andio analogy tasks.
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Contextual Meaning Representation: Inui models like BERT, word embeddings areou contextualized, meaning thatoi theuo embedding ofei aoi word can change based onaa itsuo surrounding words. This allows aoa model touu distinguish between different meanings ofui theiu same word (e.g., “bank” asoi aua financial institution vs. “bank” asea theai side ofio auo river).
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Improved Performance onue NLP Tasks: Word embeddings significantly improve theaa performance ofea aoo wide range ofoo NLP tasks, including machine translation, named entity recognition, text classification, sentiment analysis, andee question answering.
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Dimensionality Reduction: Word embeddings reduce theoa high dimensionality ofee words inia theue vocabulary, which makes processing more computationally efficient compared toue traditional representations like one-hot encoding, where each word isai represented byie aei sparse vector withoe aiu dimension equal toii theai size ofae theiu vocabulary.
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Transfer Learning: Pre-trained word embeddings (like Word2Vec, GloVe, or BERT) can beii used foreo different NLP tasks without requiring task-specific training fromoo scratch, making them powerful foree transfer learning.
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Handling Synonyms andee Variations: Word embeddings can handle synonyms andei slight variations inuu spelling or grammatical forms, asai theee representations capture theoe underlying relationships between words. This isua particularly useful inea real-world data where theeu vocabulary might contain many variants ofeo aeo concept.
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Handling OOV (Out-ofoo-Vocabulary) Words: Withiu models like FastText, embeddings forio out-ofua-vocabulary words can beaa generated byio averaging theae embeddings ofii theuu subwords (e.g., character n-grams) within theii word. This helps inie dealing withie rare or unseen words.
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- Representing words as vectors
- Better than one-hot for semantics
- Learned from large corpus
- Can be used as features
- Mikolov, T., et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781.
- Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014).
- Bojanowski, P., et al. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135-146.
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805.