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Natural Language Processing in TensorFlow

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them.

This new TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems.

Understanding Natural Language Processing in Artificial Intelligence Machine Learning

Natural language processing referred to as NLP, is the way a computer understands how the human mind thinks &

In basic terms, artificial intelligence referred to as “A.I.”, uses computer science &

data science concepts to build extremely intelligent machines capable of performing tasks that require human intelligence &

In this blog post, I intend to give a background of artificial intelligence, components of NLP &

The sentence breaking technique places sentence boundaries in large texts while the morphological segmentation divides words into groups.

Regex, the colloquial term for regular expressions, is a special sequence of characters that helps us match or find other strings or sets of strings.

The pattern concept within regex is the regular expression to be matched whereas a string is what is searched to match our word at the beginning of an expression.

Algorithms take vectors of numbers as input, therefore we need to convert documents to fixed-length vectors of numbers.

simple effective model for thinking about text documents in NLP is called the bag of words model.

The model is simple in that it throws away all of the order information in the words and focuses on the occurrence of words in a document by assigning a word a unique value.

A bag of words model allows any document we see, to be encoded as a fixed-length vector with the length of the known words.

Count vectorize in NLP allows us to both tokenize a collection of text documents as well as build a vocabulary of known words.

We use count vectorize tokenization by creating an instance of the count vectorize class, fit that instance in order to learn vocabulary from one or more documents &

This encoded vector is returned with a length of the entire vocabulary with an integer count of how many times a word popped up in that document.

This is what makes the idea of term frequency — inverse document frequency vectorization, also known as “TFIDF,” more unique &

Term frequency summarizes how often a given word appears within a document whereas inverse document downscales words that appear a lot across other documents.

TFIDF scores highlight words that very key to the overall importance of the document, words that are deemed interesting &

NLP allows analysts to sift through analysis with semantic aspects to find relevant information pertaining to that patient.

Data scientists can assess comments on social media to see how their business’s brand is performing, to see which trend is the most talked about or even where someone is while they tweet about a certain topic.

For instance, with the above statements, tweets about a business can pinpoint the best place to market a theme or what advertisements are the best to show a user when they talk about a certain topic on Twitter.

Natural Language Processing in Python 3 Using NLTK

Tech Share is Alibaba Cloud’s incentive program to encourage the sharing of technical knowledge and best practices within the cloud community.

With the increase in number of smart devices, we are creating unimaginable amounts of data — — as real time updates in our locations, logging of browsing history and comments on social networks.

Earlier this year, Forbes reported that we create about 2.5 quintillion bytes of data every day (quintillion is one followed by 18 zeroes), and 90 percent of the data that is present was created in the last two years alone!

To understand the kind of impact technology has had in our lives in recent years, here’s a comparison of scenes at Vatican City when the new Pope was being announced in 2005 and 2013.

Much of this data that we generate is unstructured, which leads to a requirement of processing to generate insights, which further drive new businesses.

If you have encountered a pile of textual data for the first time, this is the right place for you to begin your journey of making sense of the data.

You will conclude the tutorial with Named Entity Recognition (NER) and finding the statistically important words in your data through a metric called TF-IDF (term frequency — inverse document frequency).

There are three sets of tweets that NLTK’s twitter_samples provides --- a set of 5000 tweets each with negative and positive sentiments, and a third set of 20000 tweets.

To get all the tweets within any set, you can use the following code --- There is an alternate way of getting tweets from a specific time period, user or hashtag by using the Twitter APIin case you are interested.

In the next section, you would be able to understand the process of cleaning the data before using any statistical tools and then move on to common NLP techniques.

Words have different grammatical forms — — for instance, “swim”, “swam”, “swims” and “swimming” are various forms of the same verb.

It is a simple algorithm that chops off extra characters from the end of a word based on certain considerations.

From the list of words and their tags, here is the list of items and their meaning — - In general, if a tag starts with NN, the word is a noun and if it stars with VB, the word is a verb.

For the sample tweets, you should remove the following — - To search for each of the above items and remove them, you will use the regular expressions library in Python, through the package re.

It first searches for a substring that matches a URL — — starts with http:// or https://, followed by letters, numbers or special characters.

The code searches for Twitter handles — — a @ followed by any number of numbers, letters or _, and removes them.

These refinements in the process of noise removal are specific to your data and can be done only after carefully analyzing the data at hand.

If you are using the Twitter API, you may want to explore Twitter Entities, which give you the entities related to a tweet directly, grouping them into hastags, URLs, mentions and media items.

A single tweet is too small an entity to find out the distribution of words, hence, the analysis of the frequency of words would be done on all of the 20000 tweets.

If your data set is large and you do not require lemmatization, you can accordingly change the function above to either include a stemmer or avoid normalization altogether.

A few years ago, Twitter didn't have the option of adding a comment to a retweet, so an unofficial way of doing so was to follow the structure --- "comment"

To collect the named entities, you can traverse the tree generated by chunked and check whether a node has the type nltk.tree.Tree: Once you have created a deafultdict with all named entities, you can verify the output.

The TF-IDF (term frequency — inverse document frequency) is a statistic that signifies how important a term is to a document.

In this tutorial, you will work with the TF-IDF transformer of the scikit-learn package (version 0.19.1) and numpy (version 1.14.3), which you can install through pip.

When initializing the class, min_df and max_df are arguments that put thresholds for words to be present in minimum and maximum number of documents (in our case, sentences).

Demystifying Artificial Intelligence (AI): Natural Language Processing

In fact, Gartner calls chatbots the current face of AI and predicts that by 2020, more than 50% of large enterprises will use product chatbots.

The problem is that chat typically requires a human on the other end, somewhat defeating the benefits of the previously mentioned self-service tools.

One example involves analyzing a variety of questions and requests: The very first thing you have to determine is that these questions are all asking about weather, so the categorized intent is “weather.” From there, to respond correctly, you’ll have to look for more information, such as location and possibly time.

If you’re missing some of that information (e.g., “What’s the weather like?”), you could make some assumptions about location (the user’s current location) and time (now) and respond with, “The weather in Paris is currently sunny, with a chance of rain this afternoon.” Instead, you could ask for more details: “For which city would you like to know the current weather?” Now that you know what you have to do, how do you actually do it?

Fortunately, there are tools out there that can do this work for you, including identifying entities, keywords, categories, relations, semantic roles, sentiment, and emotion.

You no longer have to write these things from scratch, but whether you’re talking about modern contact center technologies, the Internet of Things, or plans for robotic world domination, you will absolutely require a strong understanding of Natural Language Processing.

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