Obie  Rowe

Obie Rowe

1598683800

Short technical information about Word2Vec, GloVe and Fasttext

Introduction

With the help of Deep Learning, Natural Language Processing (NLP) has evolved quickly. More and more companies are willing to treat texts faster and in large quantities, and this is why NLP is one of the dynamic areas in Artificial Intelligence research. However, these researchs are mainly dedicated to English : until now, most companies handle French by translation, or as one language among others through multi language algorithms. This is all the more critical since Deep Learning can lead to more accurate but less interpretable results.

Through this article, I try to give insights about the main differences between three of the most famous embeddings.

Embeddings

Word Embedding makes words “understandable” by machines. Its main goal is to capture a type of relationship between words. This relationship could be morphological, semantic, contextual, or syntactic for example. A quick way to translate words into vectors would be to convert all words into integers and then take these integers as indices for their one-hot encoding.

Let consider a one hot encoding for three sentences : “Dog and cat play”, “Dog eat meat”, “Dog and cat eat”. Here, we could give integers to all the words present in these sentences, by order of apparition, and the length of the One hot encoding would be the length of the set of words. We have there 6 unique words, the length of the vectors of the One-Hot Encoding is 6.

• Dog (1, 0, 0, 0, 0, 0),

• and (0, 1, 0, 0, 0, 0),

• cat (0, 0, 1, 0, 0, 0),

• play (0, 0, 0, 1, 0, 0),

• eat (0, 0, 0, 0, 1, 0),

• meat (0, 0, 0, 0, 0, 1)

#word-embeddings #nlp #glove-making-machine #word2vec #fasttext

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Short technical information about Word2Vec, GloVe and Fasttext
Obie  Rowe

Obie Rowe

1598683800

Short technical information about Word2Vec, GloVe and Fasttext

Introduction

With the help of Deep Learning, Natural Language Processing (NLP) has evolved quickly. More and more companies are willing to treat texts faster and in large quantities, and this is why NLP is one of the dynamic areas in Artificial Intelligence research. However, these researchs are mainly dedicated to English : until now, most companies handle French by translation, or as one language among others through multi language algorithms. This is all the more critical since Deep Learning can lead to more accurate but less interpretable results.

Through this article, I try to give insights about the main differences between three of the most famous embeddings.

Embeddings

Word Embedding makes words “understandable” by machines. Its main goal is to capture a type of relationship between words. This relationship could be morphological, semantic, contextual, or syntactic for example. A quick way to translate words into vectors would be to convert all words into integers and then take these integers as indices for their one-hot encoding.

Let consider a one hot encoding for three sentences : “Dog and cat play”, “Dog eat meat”, “Dog and cat eat”. Here, we could give integers to all the words present in these sentences, by order of apparition, and the length of the One hot encoding would be the length of the set of words. We have there 6 unique words, the length of the vectors of the One-Hot Encoding is 6.

• Dog (1, 0, 0, 0, 0, 0),

• and (0, 1, 0, 0, 0, 0),

• cat (0, 0, 1, 0, 0, 0),

• play (0, 0, 0, 1, 0, 0),

• eat (0, 0, 0, 0, 1, 0),

• meat (0, 0, 0, 0, 0, 1)

#word-embeddings #nlp #glove-making-machine #word2vec #fasttext

Word2Vec, GLOVE, FastText and Baseline Word Embeddings step

In our previous discussion we had understand the basics of tokenizers step by step. If you had not gone through my previous post i highly recommend just have a look at that post because to understand Embeddings first, we need to understand tokenizers and this post is the continuation of the previous post. I am providing the link below of my post on Tokenizers. I had explained the concepts step by step with a simple example

Understanding NLP Keras Tokenizer Class Arguments with example

As we all know preparation of Input is very important step in complete deep learning pipeline for both image and text…

medium.com

There are many more ways like countvectorizer and TF-IDF. But in both, the context of the words are not maintained that results in very low accuracy and again based on different scenarios we need to select. Countvectorizer and TF-IDF is out of scope from this discussion. Coming to embeddings, first we try to understand what the word embedding really means. As we know there are more than **171,476 **of words are there in english language and each word have their different meanings. If we want to represent 171,476 or even more words in the dimensions based on the meaning each of words, then it will result in more than 3–4 lakhs dimension because we have discussed few time ago that each and every words have different meanings and one thing to note there there is a high chance that meaning of word also change based on the context. To understand better about contexual based meaning we will look into below example

Ex- Sentence 1: An apple a day keeps doctor away. Sentence 2: The stock price of Apple is falling down due to COVID-19 pandemic. I

#word2vec #gloves #deep-learning #fasttext #data-preprocessing

Apps For Short News – The Trend Is About To Arrive

Short news apps are the future, and if they will play a defining role in changing the way consumers consume their content and how the news presenters write their report.

If you want to build an app for short news then you can check out some professional app development companies for your app project As we head into the times where mobile applications and smartphones will be used for anything and everything, the short news applications will allow the reader to choose from various options and read what they want to read.

#factors impacting the short news apps #short news applications #personalized news apps #short news mobile apps #short news apps trends #short news apps

August  Larson

August Larson

1622800020

Guide to PyTerrier: A Python Framework for Information Retrieval

Information Retrieval is one of the key tasks in many natural language processing applications. The process of searching and collecting information from databases or resources based on queries or requirements, Information Retrieval (IR). The fundamental elements of an Information Retrieval system are query and document. The query is the user’s information requirement, and the document is the resource that contains the information. An efficient IR system collects the required information accurately from the document in a compute-effective manner.

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The popular Information Retrieval frameworks are mostly written in Java, Scala, C++ and C. Though they are adaptable in many languages, end-to-end evaluation of Python-based IR models is a tedious process and needs many configuration adjustments. Further, reproducibility of the IR workflow under different environments is practically not possible with the available frameworks.

Machine Learning heavily relies on the high-level Python language. Deep learning models are built almost on one of the two Python frameworks: TensorFlow and PyTorch. Though most natural language processing applications are built on top of Python frameworks and libraries nowadays, there is no well-adaptable Python framework for the Information Retrieval tasks. Hence, here comes the need for a Python-based Information Retrieval framework that supports end-to-end experimentation with reproducible results and model comparisons.

#developers corner #information #information extraction #information retrieval #ir #learn-to-rank #ltr #pyterrier #python #random forest #ranking #terrier #xgboost

 iOS App Dev

iOS App Dev

1624162320

Significant Benefits of Geospatial Information and Big Data Analytics

Big data in GIS has critical ramifications for how we procure and leverage spatial data

In the midst of the surge of data we gather and fight with consistently, geospatial information possesses an interesting spot. Because of the networks of GPS satellites and cell towers and the rising Internet of Things, we’re able to track and correlate the location of people and items in exact manners that were impractical up to this point. Yet, putting this geospatial information to use is more difficult than one might expect.

It is frequently said that 80% of data has a spatial part. Once in a while it is a coordinate gathered from a GPS application, or essentially an address that gets geocoded to a location along a street centerline. Regardless, it is surprisingly simple to get the location of an item. With moving items, location and time are imperative to follow the article alongside some other applicable attributes (temperature, point, size, shading, and so forth). As sensors and devices become increasingly connected, data is being gathered at an uncommon rate.

The Big data pattern has drastically affected each industry, so it is little amazement that big data in GIS has critical ramifications for how we procure and leverage spatial data. Big data is definitely not a new pattern. Notwithstanding, it is turning into a bigger part of geographic data science.

Maybe perhaps the greatest change in the discussion around big data has been in the relationship between software, hardware, and expertise. One of the foremost utilizations of geospatial big data analytics has been in the humanitarian area. GIS IoT gadgets are currently being utilized across the world to gather information in conditions which were previously hard for aid workers to access and thus hard to work in.

For an illustration of the manner by which geospatial big data analytics can function admirably in this area, consider by DigitalGlobe, a non-profit organization that sources satellite information and coordinates it with different sources like social media notion and aerial imagery, use a GIS machine learning algorithm to follow activity in explicit areas and identify anomalies.

Geospatial information is not simply an area, nonetheless. Geospatial information likewise tracks how things are connected and where they are in relation to other objects. Realizing how an object changes over the long run corresponding to different items can give critical insights. For instance, how truck maintenance recommendations change depending on where a truck is found and how it is driven in the field? Utilizing all of your data to drive more intelligent maintenance plans sets aside cash, time and assets.

#big data #latest news #significant benefits of geospatial information and big data analytics #geospatial information #information