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:
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 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:
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 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.
Try this keyword extractor to see how easy it is:
In the model above, you can see how a trained AI model pulls 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 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.
Try this name extractor to automatically extract names of people from your text:
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):
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.
Go to the MonkeyLearn dashboard, click ‘Create Model’ and choose ‘Extractor’:
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):
Choose columns with the text examples that you’d like to use to train your keyword extractor:
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:
These two types of keywords we want to extract will be our tags:
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.
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.
There are a number of ways to put your model to work:
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.
April 6th, 2020