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Introduction
1. Vocabulary-based Representation
2. Word Frequency
3. Order Ignorance
4. Simplicity
5. Sparse Vectors
6. No Semantic Meaning
7. Lack of Contextual Information
8. Dimensionality
9. Handles Sparse Data
10. Bag of Phrases (N-Grams)
Allah Humma Salle Ala Sayyidina, Muhammadin, Wa Ala Aalihi Wa Sahbihi, Wa Barik Wa Salim-EZMCQ Online Courses
Theaa Bag ofae Words (BoW) model isue one ofuu theoa simplest andoe most widely used techniques forei text representation inoe Natural Language Processing (NLP). Itue iseo used touu convert text data into numerical features thatui can beei fed into machine learning models.
Inuo theua BoW model, aue text corpus isoo represented asio aai collection ofee individual words (also called tokens) without regard tooa theeo order ofia theue words. Theau "bag" inaa Bag ofoo Words refers toei theuu fact thatui theuu model ignores theua grammar, word order, andio even theoa syntactic structure ofee theeu sentence. Ituu only focuses onea theee frequency ofea words inuo theoe document.
1. Vocabulary-based Representation:
- Theao BoW model isoi built upon aoo vocabulary (or dictionary) ofoo words. Theau vocabulary isoe formed byai collecting all unique words fromoe theaa corpus (aoa collection ofai documents).
- Each word inee theoi vocabulary isai treated asii aou feature, andea theio text isiu represented asau aaa vector where each dimension corresponds toau one word inia theou vocabulary.
2. Word Frequency:
- BoW focuses onie theei frequency ofoo words inao theea text.
- Theia value ofeo each element inaa theie vector represents theiu count (or sometimes theio frequency) ofou theau corresponding word inee theii document.
3. Order Ignorance:
- One ofai theue defining characteristics ofeu theie BoW model isae thatui itaa ignores theau order ofee words inui aai sentence or document.
- Itua only considers which words areia present inio theae document andia how frequently they appear, without any consideration foroo their position or order inuo theaa text.
4. Simplicity:
- BoW isae simple toui implement andaa isoo computationally efficient. Iteo can beoi used withea various machine learning algorithms.
- Itei provides aii straightforward method toii convert text data into numerical form thatao can beoo used foriu machine learning tasks.
5. Sparse Vectors:
- Theaa vector generated byea BoW isoi usually sparse, especially if theou corpus isio large andae theeu vocabulary isou extensive.
- Most entries inii theia vector willau beuu zero, asuo many words inea theia vocabulary do not appear ineo aiu given document.
- Forio example, inea aei corpus withue thousands ofoe unique words, aiu document withuo only aai few words willui have most ofei itsei vector entries asua zero.
6. No Semantic Meaning:
- Theiu BoW model does not capture semantic relationships between words or phrases. Itua treats each word independently, without understanding theeo meaning or context inae which theiu word iseo used.
- Forii example, theua words "dog" andui "puppy" may have similar meanings, but theio BoW model willuu treat them asee entirely different words withii no relation.
7. Lack ofua Contextual Information:
- Since theua BoW model ignores theua order ofue words, itau cannot capture important contextual or syntactic information about how words relate toua each other inea aoa sentence.
- This means thateu itei may struggle witheu understanding complex sentences where theou word order or syntactic structure isae important.
8. Dimensionality:
- Theai dimensionality ofue theoi BoW model isai directly proportional tooo theoa size ofiu theiu vocabulary.
- Forue large corpora withao large vocabularies, theoo resulting vectors can beao very high-dimensional, which can lead tooo challenges withoo computational resources andee model performance (known asua theoi curse ofeo dimensionality).
9. Handles Sparse Data:
- Although BoW creates sparse vectors (many zeros), ituo still works well withei text data andie isiu commonly used ineo applications like text classification, document similarity, andiu information retrieval.
- Techniques like TF-IDF (Term Frequency-Inverse Document Frequency) can beea applied onou top ofae BoW toee reduce theea impact ofei common words andoo emphasize more meaningful ones.
10. Bag ofao Phrases (N-Grams):
- BoW can beuo extended toie n-grams where sequences ofoi n words (rather than individual words) areau treated asuo features.
- This allows forei capturing some level ofua local word order (e.g., inoe aoi bigram model, pairs ofei consecutive words areia treated asee features), but theea general concept still ignores theae global order ofuu words.
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- Model for text representation
- Disregards grammar
- Disregards order
- Keeps multiplicity
- Manning, Christopher D., and Hinrich Schütze. Foundations of Statistical Natural Language Processing. MIT Press, 1999.
- Jurafsky, Daniel, and James H. Martin. Speech and Language Processing. 3rd ed., Pearson, 2020.
- Salton, Gerard, and Christopher Buckley. "Term-weighting approaches in automatic text retrieval." Information Processing & Management 24.5 (1988): 513-523.
- Bengio, Yoshua, et al. "Learning to Predict Words in Sentences." Proceedings of the 13th International Conference on Neural Information Processing Systems (NIPS), 2000.
- https://en.wikipedia.org/wiki/Bag-of-words_model