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Roadmap NLP:
Open source libraries:
NLTK:- https://www.nltk.org/
Spacy: https://spacy.io/
- **Text Preprocessing**:
*********https://www.nltk.org/api/nltk.tokenize.html
Technics types:-
Below are part ML.
1.Tokenization --> converts our text to meaningful vectors
- Stemming
- lemmatization
- stop words
2.Bag of Words (BoW)**, **TF-IDF**
3.**Word Embeddings**: Word2Vec, GloVe, and FastText (Learn how embeddings improve similarity detection)
Below are part of deep learning we can acheive by tensorflow or pytorch
4 **Word Embeddings**: Word2Vec, GloVe, and FastText (Learn how embeddings improve similarity detection)
5.- **Sentence Embeddings**: Sentence-BERT (SBERT), Universal Sentence Encoder
============================================================
Tokenization:-
Tokenization in NLP is the process of breaking down text into smaller, manageable pieces called tokens. Tokens can represent words, subwords, sentences, or even characters, depending on the level of granularity required for the task. Tokenization is essential as it allows NLP models to process and analyze text by breaking it down into understandable units.
- Corpus -- Called as paragraphs
- Documents -- Sen tense
- Vocabulary - Unique words
- Words
### **Tokenization in Practice**
- **Tools for Tokenization**:
- **NLTK**: Provides basic word, sentence, and regex-based tokenizers.
- **spaCy**: Efficient tokenization for various languages, including handling punctuation, entities, and special cases.
- **Hugging Face Transformers**: Offers tokenizers tailored for transformer models (e.g., BERT, GPT) with options for WordPiece, BPE, and more.
- **SentencePiece**: Unsupervised tokenization with subword units, compatible with various models like T5 and XLM.
---
### **Considerations When Tokenizing Text**
- **Language Dependency**: Tokenization rules differ by language; for instance, Chinese and Japanese require segmentation methods as they lack whitespace.
- **Special Cases**: Handling of numbers, dates, URLs, or abbreviations (like “Dr.” or “U.S.”) varies by tokenizer.
- **Efficiency**: Tokenization methods can significantly impact the speed and memory requirements of NLP models.
---
Tokenization serves as the foundation for various downstream NLP tasks by creating the “input” that models learn from. The choice of tokenization technique impacts model performance and adaptability, especially in language models designed for generative AI.
Ref:- https://www.nltk.org/howto.html
https://jupyter.org/try-jupyter/lab/
#############Text Pre processing######################
Stemming:
Nothing but avoid similar words like when in para where we are taking about go going go will here go is common
to find the word the process calling as Stemming.
Types:
- Porter Stemmer.
- Regex Stemmer
- Snowball Stemmer -- its good when compare to Port Stemmer
Disadvantages:
Its lightly changing the meaning of the word like studying --> studi
But we have still some kind of issues while tokenization to do that we need to lemmatization
-----------Text Processing Lemmatization------------------
Word net lemmatize
WordNetLemmatizer.lemmatize(word, pos) uses a specified part of speech (POS) to improve accuracy. The get_wordnet_pos function helps convert POS tags from nltk.pos_tag into WordNet's format.
For each word, this code finds its POS and lemmatizes it accordingly, reducing it to its base form.
Example Output
With the code above, the words list ["running", "cats", "better", "ate"] would be lemmatized as follows:
python
Copy code
Lemmatized Words: ['run', 'cat', 'good', 'eat']
This process is essential for natural language processing, as it helps standardize words into a consistent form for analysis or machine learning tasks.
----------------------------------------------------Stop Words--------------------------------------------------
==============================Text to vector===========================
Natural Language Processing (NLP) uses various techniques to convert text into vector representations, essential for machine learning models to understand and process text data. Here are some popular methods for text-to-vector conversion:
### 1. **One-Hot Encoding**
- **Description**: Each word in the vocabulary is represented by a vector with all zeros except for a single "1" at the index corresponding to the word.
- **Limitations**: Doesn’t capture semantic relationships between words. The vector dimension can become extremely large as the vocabulary grows.
### 2. **Bag of Words (BoW)**
- **Description**: Represents text as a fixed-size vector, with each element corresponding to the frequency of a word in the document.
- **Limitations**: Ignores word order and doesn’t capture the contextual meaning.
### 3. **TF-IDF (Term Frequency-Inverse Document Frequency)**
- **Description**: Assigns a weight to each word based on its frequency in a document and how common it is across documents. Helps to reduce the influence of frequently occurring, less informative words.
- **Limitations**: Similar to BoW, it doesn’t capture word order or semantic relationships.
### 4. **Word Embeddings**
- **Description**: Dense vector representations of words in a continuous vector space, where semantically similar words have similar representations. Examples include:
- **Word2Vec**: Trained using a neural network model to capture semantic relationships by predicting context words (skip-gram) or the central word (CBOW) in a context.
- **GloVe** (Global Vectors for Word Representation): Generates word vectors by factorizing the word co-occurrence matrix, aiming to capture global statistical information.
- **FastText**: Extends Word2Vec by treating each word as a bag of character n-grams, useful for capturing subword information and handling out-of-vocabulary words.
- **Limitations**: Word embeddings are typically pre-trained on large corpora, and fine-tuning may be required for specific domains.
### 5. **Document Embeddings**
- **Description**: Extends word embeddings to represent entire documents rather than single words. Examples include:
- **Doc2Vec**: Builds on Word2Vec by training vectors for documents alongside word vectors.
- **Sent2Vec**: Averages word embeddings to produce a vector for sentences or paragraphs.
- **Limitations**: Works well for capturing global meanings but may miss out on finer, sentence-level details.
---------------------------------One Hot Encoding----------------------------
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