Dedric  Reinger

Dedric Reinger

1599167160

Introduction to Word Embeddings (NLP)

In other words, we want to find an embedding for each word in some vector space and we wanted to exhibit some desired properties.

Image for post

Representation of different words in vector space (Image by author)

For example, if two words are similar in meaning, they should be closer to each other compared to words that are not_. And, if two pair of words have a similar difference in their meanings, _they should be approximately equally separated in the embedded space.

We could use such a representation for a variety of purposes like finding synonyms and analogies, identifying concepts around which words are clustered, classifying words as positive, negative, neutral, etc. By combining word vectors, we can come up with another way of representing documents as well.


Word2Vec — The General Idea

Word2Vec is perhaps one of the most popular examples of word embeddings used in practice. As the name Word2Vec indicates, it transforms words to vectors. But what the name doesn’t give away is how that transformation is performed.

#machine-learning #data-science #nlp #word-embeddings #artificial-intelligence

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Buddha Community

Introduction to Word Embeddings (NLP)

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.


Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.


#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools

Dedric  Reinger

Dedric Reinger

1599167160

Introduction to Word Embeddings (NLP)

In other words, we want to find an embedding for each word in some vector space and we wanted to exhibit some desired properties.

Image for post

Representation of different words in vector space (Image by author)

For example, if two words are similar in meaning, they should be closer to each other compared to words that are not_. And, if two pair of words have a similar difference in their meanings, _they should be approximately equally separated in the embedded space.

We could use such a representation for a variety of purposes like finding synonyms and analogies, identifying concepts around which words are clustered, classifying words as positive, negative, neutral, etc. By combining word vectors, we can come up with another way of representing documents as well.


Word2Vec — The General Idea

Word2Vec is perhaps one of the most popular examples of word embeddings used in practice. As the name Word2Vec indicates, it transforms words to vectors. But what the name doesn’t give away is how that transformation is performed.

#machine-learning #data-science #nlp #word-embeddings #artificial-intelligence

Introduction to Word Embeddings

Word embedding is a method to capture the “meaning” of the word via low dimension vector and it can be used in a variety of tasks in Natural Language Processing (NLP).

Before beginning word embedding tutorial we should have an understanding of vector space and similarity matrix.

Vector Space

A sequence of numbers that is used to identify a point in space is called vector and if we have a whole bunch of vectors that all belong to the same dataset it will be called a** vector space**.

Words in the text can also be represented in the higher dimension in vector space where words having the same meaning will have similar representations. For example,

deep learning word embedding machine learning data science text preprocessing

photo by Allision Parrish from Github

The above image shows a vector representation of words on the scale of cuteness and size of animals. we can see that there is a semantic relationship between words on bases of similar properties. It is difficult to represent the higher dimensional relationship between words but the maths behind is the same so it works similarly in a higher dimension also.

Similarity matrix

It is used to calculate the distance between vectors in the vector space. it measures similarity or distance between two data points in vector space. This allows us to capture words that are used in similar ways to result in having similar representation naturally capturing their meaning. there is a lot of similarity matrix available but we will discuss Euclidean distance and Cosine similarity.

Euclidean distance

One way to calulate how far two data points are in vector space is to calculate Euclidean distance.

import math
def distance2d(x1, y1, x2, y2):
    return math.sqrt((x1 - x2)**2 + (y1 - y2)**2)

So, the distance between “capybara” (70, 30) and “panda” (74, 40) from the above image example:

Image for post

… is less than the distance between “tarantula” and “elephant” from the above image example:

Image for post

This shows that “pandas” and “capybara” are more similar as compared to “tarantula” and “elephant”.

Cosine similarity

It is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them.

from numpy import dot
from numpy.linalg import norm

cos_sim = dot(a, b)/(norm(a)*norm(b))

#data-science #machine-learning #deep-learning #word-embeddings #nlp #deep learning

Noah  Rowe

Noah Rowe

1597026240

NLP Representation Techniques

I have explained the bag of words and TFIDF in my first blog. You can find it here**. **Then we discussed some of the issues with TFIDF and learned about word embedding in Part 2, Also how to generate them using Word2Vec. If you want to read about that you can find it here.

I will be showing you some advanced methods and embedding to go for when you are working on an industrial scale project. These include Glove, BERT word embedding, BERT sentence embedding, and Multilingual embedding.

Glove

The Glove was a model proposed by Stanford University in 2014. The goal is very much the same i.e to learn word embeddings and relations between the words. You can read more about Glove here.

Luckily we don’t have train this model from scratch so we can just use the pre-trained embeddings for our use case. All you have to do it is Click **here **and then go to the **Download pre-trained word vectors **section.

You will find these options like Wikipedia, Twitter which is the type of training data they have used to train the model so the notion of the word embedding will be dependent on the corpus. You will also find this 400k vocab, 1.9 M vocab, or 2.2 M vocab, these are the number of words that they have in the vocabulary for that model.

I have downloaded the Wikipedia version with 400K Vocab. You will find these four files which are names something like glove50d, glove100d, or glove300d.

Image for post

These 50d,100d, or 200d indicates the size of the vector for each word if it is 50 dimensions or 100 dimensions and so on.

Image for post

Once you open this file you will find that first value in each row is the word and the rest of the row is the word embedding for that word. Now we just need to read this file and extract out the word embeddings.

Image for post

I have used this piece of code to convert the text file into a dictionary of words as keys and its word embeddings as the corresponding value.

You can see we are just looping through each line in the file and setting the first item after splitting as word and remaining as an array. I have used Numpy here as it is efficient and fast.

Now we have the word embeddings for each word in the corpus. You can apply all the techniques we discussed in Part2 and use them as per your need.

There is a variation of size in the word embeddings as 50 dimensions or 100 dimensions. Generally, the idea is if we use word embeddings with a bigger size of the vector we have more information about the word so we should get a boost in the accuracy as the model has more information to learn from.

I was working on a simple sentimental analysis using LSTM neural network. I tried moving from 50 dimensions to 100 dimensions and I got a boost of 5–7 % in the accuracy but as I increased it to 300 dimensions there was not as much of the boost, I only got a boost of 0.03 %. You can try it on your project and see if it helps.

#data-science #nlp #sentence-embedding #word-embeddings #artificial-intelligence

Murray  Beatty

Murray Beatty

1596855180

Legal Applications of Neural Word Embeddings

A fundamental issue with LegalTech is that words — the basic currency of all legal documentation — are a form of unstructured data that cannot be intuitively understood by machines. Therefore, in order to process textual documents, words have to be represented by vectors of real numbers.

Traditionally, methods like bag-of-words (BoW) map word tokens/n-grams to term frequency vectors, which represent the number of times a word has appeared in the document. Using one-hot encoding, each word token/n-gram is represented by a vector element and marked 0, 1, 2 etc depending on whether or the number of times that a word is present in the document. This means that if a word is absent in the corpus vocabulary, the element will be marked 0, and if it is present once, the element will be marked 1 etc.

#legaltech #legal-ai-software-market #word-embeddings #legal-ai #nlp