Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. I don't need to detail what spaCy does, it is perfectly summarized by spaCy in this article: **spaCy 101: Everything you need to know**.
`spaCy` is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. I don't need to detail what spaCy does, it is perfectly summarized by spaCy in this article: **spaCy 101: Everything you need to know**.
Today, we will learn to use a pre-trained embedding to convert a text into a vector to compute similarity between words or sentences. Remember, embeddings translate large sparse vectors into a lower-dimensional space that preserves semantic relationships.
Today, we will learn to use a pre-trained embedding to convert a text into a vector to compute similarity between words or sentences. Remember, embeddings translate large sparse vectors into a lower-dimensional space that preserves semantic relationships.
Word embeddings is a technique where individual words of a domain or language are represented as real-valued vectors in a lower dimensional space. The BoW representation's dimension depends on the size of the vocabulary. But it can easily reach 10k words. We will also learn to use NER and Part-of-speech. NER allows to identify and segment the named entities and classify or categorize them under various predefined classes. Part-of-speech is a special label assigned to each token (word) in a text corpus to indicate the part of speech and often also other grammatical categories such as tense, number (plural/singular), case etc.
Word embeddings is a technique where individual words of a domain or language are represented as real-valued vectors in a lower dimensional space. The BoW representation's dimension depends on the size of the vocabulary. But it can easily reach 10k words. We will also learn to use NER and Part-of-speech. NER allows to identify and segment the named entities and classify or categorize them under various predefined classes. Part-of-speech is a special label assigned to each token (word) in a text corpus to indicate the part of speech and often also other grammatical categories such as tense, number (plural/singular), case etc.
@ -20,11 +20,11 @@ Word embeddings is a technique where individual words of a domain or language ar
- Python 3.x
- Python 3.x
- Jupyter or JupyterLab
- Jupyter or JupyterLab
- Pandas
- Pandas
- Spacy
- spaCy
- Scikit-learn
- Scikit-learn
- Matplotlib
- Matplotlib
I suggest to use the most recent libraries.
I suggest using the most recent libraries.
### **Resources**
### **Resources**
@ -41,15 +41,15 @@ I suggest to use the most recent libraries.
The goal of this exercise is to set up the Python work environment with the required libraries.
The goal of this exercise is to set up the Python work environment with the required libraries.
**Note:** For each quest, your first exercice will be to set up the virtual environment with the required libraries.
**Note:** For each quest, your first exercise will be to set up the virtual environment with the required libraries.
I recommend to use:
I recommend to use:
- the **last stable versions** of Python.
- the **last stable versions** of Python.
- the virtual environment you're the most confortable with. `virtualenv` and `conda` are the most used in Data Science.
- the virtual environment you're the most comfortable with. `virtualenv` and `conda` are the most used in Data Science.
- one of the most recents versions of the libraries required
- one of the most recent versions of the libraries required
1. Create a virtual environment named with a version of Python >= `3.8`, with the following libraries: `pandas`, `jupyter`, `spacy`, `sklearn`, `matplotlib`.
1. Create a virtual environment named with a version of Python >= `3.8`, with the following libraries: `pandas`, `jupyter`, `spaCy`, `sklearn`, `matplotlib`.
---
---
@ -57,7 +57,7 @@ I recommend to use:
# Exercise 1: Embedding 1
# Exercise 1: Embedding 1
The goal of this exercise is to learn to load an embedding on SpaCy.
The goal of this exercise is to learn to load an embedding on `spaCy`.
1. Install and load `en_core_web_sm` version `3.4.0` [embedding](https://github.com/explosion/spacy-models/releases/tag/en_core_web_sm-3.4.0). Compute the embedding of `car`.
1. Install and load `en_core_web_sm` version `3.4.0` [embedding](https://github.com/explosion/spacy-models/releases/tag/en_core_web_sm-3.4.0). Compute the embedding of `car`.
@ -67,7 +67,7 @@ The goal of this exercise is to learn to load an embedding on SpaCy.
# Exercise 2: Tokenization
# Exercise 2: Tokenization
The goal of this exercise is to learn to tokenize a document using Spacy. We did this using NLTK yesterday.
The goal of this exercise is to learn to tokenize a document using `spaCy`. We did this using NLTK yesterday.
1. Tokenize the text below and print the tokens
1. Tokenize the text below and print the tokens
@ -82,7 +82,7 @@ The goal of this exercise is to learn to tokenize a document using Spacy. We did
# Exercise 3: Embeddings 2
# Exercise 3: Embeddings 2
The goal of this exercise is to learn to use SpaCy embedding on a document.
The goal of this exercise is to learn to use `spaCy` embedding on a document.
1. Compute the embedding of all the words in this sentence. The language model considered is `en_core_web_md`
1. Compute the embedding of all the words in this sentence. The language model considered is `en_core_web_md`
@ -130,7 +130,7 @@ Apple was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in April 1976 t
1. Extract all named entities in the text as well as the label of the named entity.
1. Extract all named entities in the text as well as the label of the named entity.
2. The NER is also useful to remove ambigous entities. From a conceptual standpoint, disambiguation is the process of determining the most probable meaning of a specific phrase. For example in the sentence below, the word `apple` is present twice in the sentence. The first time to mention the fruit and the second to mention a company. Run the NER on this sentence and print the named entity, the `start_char`, the `end_char` and the label of the named entity.
2. The NER is also useful to remove ambiguous entities. From a conceptual standpoint, disambiguation is the process of determining the most probable meaning of a specific phrase. For example in the sentence below, the word `apple` is present twice in the sentence. The first time to mention the fruit and the second to mention a company. Run the NER on this sentence and print the named entity, the `start_char`, the `end_char` and the label of the named entity.
```
```
Paul eats an apple while watching a movie on his Apple device.
Paul eats an apple while watching a movie on his Apple device.
The goal od this exercise is to learn to use the Part-of-speech tags (**POS TAG**) using Spacy. As explained in wikipedia, the POS TAG is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.
The goal of this exercise is to learn to use the Part-of-speech tags (**POS TAG**) using `spaCy`. As explained on Wikipedia, the POS TAG is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.
Example
Example
@ -157,6 +157,6 @@ The sentence: **"Heat water in a large vessel"** is tagged this way after the PO
- large adj (noun)
- large adj (noun)
- vessel noun
- vessel noun
The data `news_amazon.txt` used is a newspaper about Amazon.
The data `news_amazon.txt` used is a newspaper about Amazon.
1. Return all sentences mentioning **Bezos** as a NNP (tag).
1. Return all sentences mentioning **Bezos** as a NNP (tag).
nprimo
11 months ago
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