AI News, Machine Learning algorithms: Working with text data

Machine Learning algorithms: Working with text data

JAXenter: What is the difference between image and text from a machine’s point of view?

Christoph Henkelmann: Almost all ML methods, especially neural networks, want tensors (multidimensional arrays of numbers) as input.

In case of an image the transformation is obvious, we already have a three-dimensional array of pixels (width x height x color channel), i.e.

Text and words exist at a higher level of meaning, for example, if you simply enter Unicode-encoded letters as numbers in the net, the jump from coding to semantics is too “high”.

after all, it is intended to finally solve all the coding problems from the early days of word processing.

If you use standard methods of some programming languages to split text from different sources, you suddenly wonder why words still stick together.

basically the same as with a text file, through methods where individual words are encoded as the smallest unit, to methods, where a tensor is generated from an entire document, which is actually more of a “fingerprint”

Christoph Henkelmann: Exactly, much more than with images or audio, the pre-processing of text has an effect on the semantic level at which the process moves.

Sometimes preprocessing itself is already a kind of machine learning, so that we can already answer questions, only because we have coded the text differently.

How to Clean Text for Machine Learning with Python

You cannot go straight from raw text to fitting a machine learning or deep learning model.

In fact, there is a whole suite of text preparation methods that you may need to use, and the choice of methods really depends on your natural language processing task.

In this tutorial, you will discover how you can clean and prepare your text ready for modeling with machine learning.

You can download the ASCII text version of the text here: Download the file and place it in your current working directory with the file name “metamorphosis.txt“.

The file contains header and footer information that we are not interested in, specifically copyright and license information.

The start of the clean file should look like: One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin.

The file should end with: And, as if in confirmation of their new dreams and good intentions, as soon as they reached their destination Grete was the first to get up and stretch out her young body.

After actually getting a hold of your text data, the first step in cleaning up text data is to have a strong idea about what you’re trying to achieve, and in that context review your text to see what exactly might help.

into memory as follows: Running the example loads the whole file into memory ready to work with.

Clean text often means a list of words or tokens that we can work with in our machine learning models.

Another approach might be to use the regex model (re) and split the document into words by selecting for strings of alphanumeric characters (a-z, A-Z, 0-9 and ‘_’).

We can create an empty mapping table, but the third argument of this function allows us to list all of the characters to remove during the translation process.

For example: We can put all of this together, load the text file, split it into words by white space, then translate each word to remove the punctuation.

It provides good tools for loading and cleaning text that we can use to get our data ready for working with machine learning and deep learning algorithms.

You can install NLTK using your favorite package manager, such as pip: After installation, you will need to install the data used with the library, including a great set of documents that you can use later for testing other tools in NLTK.

You could first split your text into sentences, split each sentence into words, then save each sentence to file, one per line.

Running the example, we can see that although the document is split into sentences, that each sentence still preserves the new line from the artificial wrap of the lines in the original document.

Some applications, like document classification, may benefit from stemming in order to both reduce the vocabulary and to focus on the sense or sentiment of a document rather than deeper meaning.

There is a nice suite of stemming and lemmatization algorithms to choose from in NLTK, if reducing words to their root is something you need for your project.

Because the source text for this tutorial was reasonably clean to begin with, we skipped many concerns of text cleaning that you may need to deal with in your own project.

Hopefully, you can see that getting truly clean text is impossible, that we are really doing the best we can based on the time, resources, and knowledge we have.

Ideally, you would save a new file after each transform so that you can spend time with all of the data in the new form.

Recently, the field of natural language processing has been moving away from bag-of-word models and word encoding toward word embeddings.

The benefit of word embeddings is that they encode each word into a dense vector that captures something about its relative meaning within the training text.

This means that variations of words like case, spelling, punctuation, and so on will automatically be learned to be similar in the embedding space.

In my experience, it is usually good to disconnect (or remove) punctuation from words, and sometimes also convert all characters to lowercase.

All these pre-processing steps aim to reduce the vocabulary size without removing any important content (which in some cases may not be true when you lowercase certain words, ie.

Natural Language Processing (NLP) Techniques for Extracting Information

It offers a deep-dive into some essential data mining tools and techniques for harvesting content from the Internet and turning it into significant business insights.

Once you have identified, extracted, and cleansed the content needed for your use case, the next step is to have an understanding of that content. In many use cases, the content with the most important information is written down in a natural language (such as English, German, Spanish, Chinese, etc.) and not conveniently tagged.

To extract information from this content you will need to rely on some levels of text mining, text extraction, or possibly full-up natural language processing (NLP) techniques.

Basic processing will be required to convert this character stream into a sequence of lexical items (words, phrases, and syntactic markers) which can then be used to better understand the content.

Basis Technology offers a fully featured language identification and text analytics package (called Rosette Base Linguistics) which is often a good first step to any language processing software.

Our NLP tools include tokenization, acronym normalization, lemmatization (English), sentence and phrase boundaries, entity extraction (all types but not statistical), and statistical phrase extraction.

Complex pattern-based extraction – good for people names (made of known components), business names (made of known components) and context-based extraction scenarios (e.g.

After having done numerous NLP projects, we’ve come up with a flowchart to help you decide if your requirements are likely to be manageable with today’s NLP techniques.  Once you have decided to embark on your NLP project, if you need a more holistic understanding of the document this is a “macro understanding.” This is useful for: A

Note that “Text Processing Libraries” will need to be included in this architecture to handle all of the basic NLP functions described above in “STEP 1:  The Basics.” This can include multiple open source projects working together, or one or two vendor packages.

 Top Down – determine Part of Speech, then understand and diagram the sentence into clauses, nouns, verbs, object and subject, modifying adjectives and adverbs, etc., then traverse this structure to identify structures of interest Sample top-down output from Google Cloud Natural Language API(Right-click on the image and select 'Open image in new tab' for better image clarity)

Also notice that a second step (which requires custom programming) is required to take this graph and identify object / action relationships suitable for exporting to a graph or relational database.

 Statistical – similar to bottoms-up, but matches patterns against a statistically weighted database of patterns generated from tagged training data.

Note that these patterns may be entered manually, or they may be derived statistically (and weighted statistically) using training data or inferred using text mining and machine learning.

This usually involves: By saving this information throughout the process, you can trace back from the outputs all the way back to the original web page or file which provided the content that was processed.

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