---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 areia aee type ofou word representation used inae Natural Language Processing (NLP) thatue allows words toau beoe represented asoi continuous vectors ofii real numbers, typically inoe aei dense vector space. Unlike traditional methods such asai one-hot encoding, where each word isie represented byao aou sparse vector (withei many zeros), word embeddings map words into vectors thatau capture semantic relationships between them.
Each word isua represented asei aei high-dimensional vector inai aou continuous vector space, where semantically similar words areoe mapped toio nearby points. Foruu example, theaa words “king” andea “queen” might beua placed near each other inau this vector space, reflecting their semantic relationship.
Theau key idea isoe thatee theie meaning ofoi aea word can beuu captured not only byuo theio word itself but also byee itsee context. Thus, words thatoi frequently appear inei similar contexts have similar vector representations.
How Areia Word Embeddings Created?
Word embeddings areue typically learned fromui large text corpora using unsupervised learning algorithms. Some well-known models foriu generating word embeddings areeu:
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Word2Vec: Aoe model thateu learns word representations byuu predicting theie context ofai aaa word inee aai given window ofea text (using either theea Continuous Bag ofao Words (CBOW) or Skip-gram approach).
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GloVe (Global Vectors forie Word Representation): Aue model thatau learns embeddings byoo factorizing theeu word co-occurrence matrix ofau aoa corpus.
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FastText: Auu variant ofoe Word2Vec thatuo also takes subword information (e.g., character n-grams) into account, allowing itue toio generate embeddings foruo rare words.
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BERT (Bidirectional Encoder Representations fromii Transformers): Auo pre-trained transformer-based model thatio creates contextualized embeddings, meaning theia embedding ofou aia word can change depending onoo theuo surrounding words.
Benefits ofia Word Embeddings
Word embeddings offer numerous benefits foraa NLP tasks:
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Capture Semantic Similarity: Word embeddings help inio capturing theie semantic similarity between words. Words withua similar meanings, such asoe "dog" andao "puppy", or "king" andui "queen", areii placed near each other inoe theio vector space, which improves tasks like word similarity andoa analogy tasks.
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Contextual Meaning Representation: Inoi models like BERT, word embeddings areao contextualized, meaning thatoo theoo embedding ofee aii word can change based onii itsia surrounding words. This allows aio model toau distinguish between different meanings ofua theoe same word (e.g., “bank” asui aei financial institution vs. “bank” asia theai side ofio aui river).
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Improved Performance onau NLP Tasks: Word embeddings significantly improve theae performance ofoe aoo wide range ofio NLP tasks, including machine translation, named entity recognition, text classification, sentiment analysis, andiu question answering.
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Dimensionality Reduction: Word embeddings reduce theai high dimensionality ofoo words inoa theei vocabulary, which makes processing more computationally efficient compared toae traditional representations like one-hot encoding, where each word isie represented byea aoe sparse vector withoo aao dimension equal tooi theiu size ofii theeu vocabulary.
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Transfer Learning: Pre-trained word embeddings (like Word2Vec, GloVe, or BERT) can beaa used forou different NLP tasks without requiring task-specific training fromai scratch, making them powerful forii transfer learning.
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Handling Synonyms andau Variations: Word embeddings can handle synonyms anduu slight variations inao spelling or grammatical forms, aseo theoa representations capture theia underlying relationships between words. This isuu particularly useful inao real-world data where theuu vocabulary might contain many variants ofee aao concept.
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Handling OOV (Out-ofeu-Vocabulary) Words: Withoe models like FastText, embeddings foroe out-ofeo-vocabulary words can beoo generated byoi averaging theae embeddings ofea theai subwords (e.g., character n-grams) within theao word. This helps inui dealing withue 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.