Text Extractor Tool: Extract Keywords with Machine Learning

Text extractors use AI to identify and extract relevant or notable pieces of information from within documents or online resources. Most simply, text extraction pulls important words from written texts and images.

Try out this free keyword extraction tool to see how it works.

Common uses of text extraction are:

    • Keyword extraction (to identify the most relevant words in a text)
    • Named entity extraction (to identify names of people, places, or businesses)
    • Summary Extraction (to summarize a text)
    • Text-from-image extraction, otherwise known as optical character recognition (OCR) (to lift text directly from an image, for example, PDFs)

Text extraction differs from text classification, in that text classification reads a text for meaning, then assigns predefined tags, based on the content, to categorize texts by topic, sentiment, language, etc. The result is usually not present within the text and the classifiers make predictions based on previous samples.

Text extraction, on the other hand, recognizes relevant information that appears within a text or image, and models are trained to tag predefined entities. 

In short, classifiers categorize information, whereas extractors highlight entities. 

Text Extraction with Machine Learning for Businesses

Text extraction is useful for businesses because it uses automated AI programs to analyze documents and online conversations that may otherwise take hundreds of employee hours to accomplish. Manually scanning through customer comments and surveys to extract important information, for example, is time-consuming, tedious, and inefficient.

Machine learning extractors can be trained for all sorts of industry needs: 

  • Scan the most relevant words in the subject and body of incoming support tickets. 
  • Find out which topics are being mentioned most often in your tweets to get a sense of what people are saying about your brand. 
  • Extract information from product descriptions (e.g. leather, sizes 4-7, unisex) in preparation for data entry. 

Examples of Text Extraction

MonkeyLearn offers a number of user-friendly AI solutions in text extraction that can be put to work to increase productivity, pinpoint obstacles, and improve customer service. 

Keyword Extraction

Keyword extraction extracts relevant terms and phrases from within a text. These are terms that help to summarize the text, are significant to the writer’s viewpoint, or significant to the overall concept of the text.

In the example above, you can see how a trained AI model pulled the most important words and phrases, allowing the user to quickly understand the meaning of the text without reading all of it. 

Imagine putting keyword extraction to work analyzing thousands of customer questionnaires or social media posts in a matter of seconds. 

MonkeyLearn’s advanced machine learning technology allows you to train models to your explicit needs, so you only get the information you want. 

Named Entity Extraction

Named entity extractors locate and classify “named entities,” like names, organizations, locations, and monetary values, in unstructured texts. AI programs recognize these titles and values through their unique word sequences, and then classify them as instructed. More than one entity can be pulled from an individual text to create multiple classification fields.

Named entity extraction can be used to create customer databases and provide feedback, scan news content to reveal important data, and provide directed content recommendations through customer data analysis. 

See below how email addresses can be extracted, for example:

Summary Extraction

Summary extractors are AI models created to scan a full text and provide a shortened summary. Using natural language processing (NLP) technology and statistical algorithms, summary extraction is able to create a summary that keeps the gist of the original text. 

Summaries are written in sentence form, using only text that appears in the original scanned document. Summary extraction can be used to scan daily news articles, read through whole libraries of documents, or aid in SEO creation by finding whole sentences that are used commonly within your industry.

See example of summary extraction below (note that original text is much longer than shown):

Your Simple Text Extractor Tutorial

Now that you’ve learned about text extractors, are you ready to try one out? You can use MonkeyLearn’s pre-trained keyword extractor, or follow along below to learn how to train your own – it’s free and easy.

Simply sign up to MonkeyLearn and follow below.

  • Create a new model

Go to the MonkeyLearn dashboard, click ‘Create Model’ and choose ‘Extractor’:

  • Import text data

You can upload an Excel or CSV file, or upload your data directly from an app like Twitter, Gmail, or Zendesk. For this tutorial, we’ll use a CSV file of hotel reviews (a CSV file available in our data library):

  • Select data to train your model:

Choose columns with the text examples that you’d like to use to train your keyword extractor:

  • Define your tags:

Create tags for your keyword extractor to categorize words or expressions that you want to pull from text. For example, in this case we’d like to extract two types of keywords from the hotel reviews:

  • Aspect: these are words and expressions that refer to the feature or topic the hotel review is talking about. For example, in the following review ‘The bed is really comfortable’ the aspect keyword would be ‘bed’.
  • Quality: these are keywords that talk about the state or condition of the hotel or one of its aspects. In the example above ‘The bed is really comfortable’ the quality keyword would be ‘comfortable’.

These two types of keywords we want to extract will be our tags:

  •    Train your text extractor

Now you’ll start tagging relevant words in the text to train your keyword extractor. Just check the box next to the tag you want and select the appropriate words. This is where machine learning begins – you’re training your model to make its own predictions. Once you’ve tagged a few examples, the text extractor starts making its own predictions. 

Once your extractor is trained, give it a name.

  • Put your new model to the test

Test your model to see how it works on unseen data. If it’s not producing satisfactory results, you can keep training it with more data. The more data training you do, the more accurate your model will be. You can check the performance of your extractor: click ‘build’ and see stats like F1 Score, Precision, and Recall for each of your tags.

  • Make your model work for you

There are a number of ways to put your model to work:

  • Demo: you just have to paste a text, and the model will automatically detect and highlight the different features.
  • Batch: if you want to analyze several pieces of data, you can upload a CSV or an Excel file. The keyword extraction model will add a new column to the document with all the predicted keywords.
  • API: developers can connect to the MonkeyLearn API and obtain extracted keywords as a JSON file.
  • Integrations: you can use Zapier, RapidMiner, Google Sheets or Zendesk as a data source, and connect it with MonkeyLearn for your keyword extraction process.

The Takeaway

Text extraction can be put to use in a multitude of ways to analyze data and help your company move forward in the age of AI. By enabling your employees to concentrate on only the data that matters, you’ll be able to increase efficiency and free employees from tedious tasks, such as data entry. 

Use powerful resources, like keyword extraction, named entity extraction, and summary extraction to benefit your business, customers, and employees.

MonkeyLearn has you covered with user-friendly tools. Sign up for free and get started right away.

Inés Roldós

Inés Roldós

Marketing @MonkeyLearn. Business Administration student.

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