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- Introduction to NLP
- Installing NLTK
- An Introduction to N-grams
- NLP вЂ“ Stop Words
- Stemming and Lemmatization
- Word Tokenization with NLTK
- TfidfVectorizer for text category
- CountVectorizer for text category
- Regular Expression for Text Cleaning in NLP
- Text Data Cleaning & Preprocessing
- Various Tokenization Way Of Text Processing
- Introduction to Term Embeddings
- Cosine Similarity
- Jaccard Similarity
- NLTK вЂ“ WordNet
- Text Preprocessing: Handle Emoji & Emoticon
- Text Preprocessing: Removal of Punctuations
- TensorFlow : Text Classification
- Develop the writing Classifier with TensorFlow Hub
- Introduction to BERT
- Tensorflow : BERT Fine-tuning with GPU
Cosine Similarity вЂ“ Text Similarity Metric
Text Similarity has got to regulate how the 2 text papers near to one another with regards to their meaning or context.
There are many different text similarity metric exist such as for example Cosine similarity, Euclidean distance and Jaccard Similarity. Every one of these metrics have actually their very own specification determine the similarity between two inquiries.
In this guide, you’ll discover the similarity that is cosine with instance. You shall additionally arrive at comprehend the math behind the cosine similarity metric with instance. Please relate to this guide to explore the Jaccard Similarity.
Cosine similarity is amongst the metric to gauge the text-similarity between two papers regardless of their size in Natural language Processing. a term is represented into a vector type. The written text papers are represented in n-dimensional vector area.
Mathematically, Cosine similarity metric measures the cosine regarding the angle between two n-dimensional vectors projected in a space that is multi-dimensional. The Cosine similarity of two papers will range between 0 to at least one. In the event that Cosine similarity rating is 1, it indicates two vectors have actually the orientation that is same.
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