Branch-AI/one_md_per_day_format/piscine/Week3/w3day05.md

Week 3 D02 Piscine AI - Data Science

Table of Contents:

Introduction

Embeddings ...

Library: 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.

There are many type of language models pre-trained in Spacy. Each has its specificities depending on the hypothesis taken.

Historical

Rules

Ressources

Exercise 1 Embedding 1

The goal of this exercise is to learn to load an embedding on SpaCy.

1. Install and load en_core_web_sm embedding. Compute the embedding of car.

Correction

1. This question is validated if the embedding's shape is (96,) and the vector 20 first values are:

array([ 1.0522802e+00,  1.4806499e+00,  7.7402556e-01,  1.0373484e+00,
        4.1474584e-01, -5.7604712e-01,  3.0856287e+00,  1.4814860e-01,
       -3.0170975e+00,  3.4453702e+00,  6.3330579e-01,  1.1655847e+00,
        3.8489954e+00,  2.3469532e+00,  5.0532556e-01, -1.9386177e+00,
        9.7954911e-01,  2.3573284e+00, -1.9812435e-03,  5.5679207e+00],
      dtype=float32)

Exercise 2: Tokenization

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

       text = "Tokenize this sentence. And this one too."
   
   

Correction

1. The question is validated if the tokens printed are:

   Tokenize
   this
   sentence
   .
   And
   this
   one
   too
   .
   

Exercise 3 Embeddings 2

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

"laptop computer coffee tea water liquid dog cat kitty"

2. Plot the pairwise cosine distances between all the words in a HeatMap.

https://medium.com/datadriveninvestor/cosine-similarity-cosine-distance-6571387f9bf8

Correction

1. This question is validated if the embeddings of each word has a shape of (300,) and if the first 20 values of the embedding of laptop are:

   array([-0.37639 , -0.075521,  0.4908  ,  0.19863 , -0.11088 , -0.076145,
       -0.30367 , -0.69663 ,  0.87048 ,  0.54388 ,  0.42523 ,  0.18045 ,
       -0.4358  , -0.32606 , -0.70702 , -0.069127, -0.42674 ,  2.4147  ,
           0.26806 ,  0.46584 ], dtype=float32)
   
   

2. This question is validated if the output is

Exercise 4 Sentences' similarity

The goal of this exerice is to learn to compute the similarity between two sentences. As explained in the documentation: The word embedding of a full sentence is simply the average over all different words. This is how similarity works in SpaCy. This small use case is very interesting because if we build a corpus of sentences that express an intention as buy shoes, then we can detect this intention and use it to propose shoes advertisement for customers. The language model used in this exercise is en_core_web_sm.

1. Compute the similarities (3 in total) between these sentences:

   sentence_1 = "I want to buy shoes"
   sentence_2 = "I would love to purchase running shoes"
   sentence_3 = "I am in my room"
   
   

Correction

2. This question is validated if the similarities between the sentences are:

   sentence_1 <=> sentence 2 : 0.7073220863266589
   sentence_1 <=> sentence 3: 0.42663743263528325
   sentence_2 <=> sentence 3: 0.3336274235605957
   
   

Exercise 5: NER

The goal of this exercise is to learn to use a Named entity recognition algorithm to detect entities.

Apple Inc. is an American multinational technology company headquartered in Cupertino, California, that designs, develops, and sells consumer electronics, computer software, and online services. It is considered one of the Big Five companies in the U.S. information technology industry, along with Amazon, Google, Microsoft, and Facebook.
Apple was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in April 1976 to develop and sell Wozniak's Apple I personal computer, though Wayne sold his share back within 12 days. It was incorporated as Apple Computer, Inc., in January 1977, and sales of its computers, including the Apple I and Apple II, grew quickly.

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.

Paul eats an apple while watching a movie on his Apple device.

https://en.wikipedia.org/wiki/Named-entity_recognition

Correction

1. This question is validated if the ouptut of the NER is

   Apple Inc. ORG
   American NORP
   Cupertino GPE
   California GPE
   Five CARDINAL
   U.S. GPE
   Amazon ORG
   Google ORG
   Microsoft ORG
   Facebook ORG
   Apple ORG
   Steve Jobs PERSON
   Steve Wozniak PERSON
   Ronald Wayne PERSON
   April 1976 DATE
   Wozniak PERSON
   Apple ORG
   Wayne PERSON
   12 days DATE
   Apple Computer, Inc. ORG
   January 1977 DATE
   Apple ORG
   Apple II ORG
   

2. This question is validated if the output shows that the first occurence of apple is not a named entity. In my case here is what the NER returns:

   Paul 1 5 PERSON
   Apple 50 55 ORG
   
   

Exercise 6 Part-of-speech tags

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.

Example

The sentence: "Heat water in a large vessel" is tagged this way after the POS TAG:

• heat verb (noun)
• water noun (verb)
• in prep (noun, adv)
• a det (noun)
• large adj (noun)
• vessel noun

The data news_amazon.txt used is a news paper about Amazon.

1. Return all sentences mentioning Bezos as a NNP (tag).

Correction

1. This question is validated if the sentences outputed are:

   INFO:  Bezos PROPN NNP
   Sentence:  Amazon (AMZN) enters 2021 with plenty of big opportunities, but is losing its lauded Chief Executive Jeff Bezos, who announced his plan to step aside in the third quarter.
   
   
   INFO:  Bezos PROPN NNP
   Sentence:  Bezos will hand off his role as chief executive to Andy Jassy, the CEO of its cloud computing unit.
   
   
   INFO:  Bezos PROPN NNP
   Sentence:  He's not leaving, as Bezos will transition to the role of Executive Chairman and remain active.
   
   
   INFO:  Bezos PROPN NNP
   Sentence:  "When you look at our financial results, what you're actually seeing are the long-run cumulative results of invention," Bezos said in written remarks with the Amazon earnings release.