It also is a nice method for quickly removing stop words. This allows us to specify the length of the keywords and make them into keyphrases. Although many focus on noun phrases, we are going to keep it simple by using Scikit-Learns CountVectorizer. We start by creating a list of candidate keywords or keyphrases from a document. I believe that using a document about a topic that the readers know quite a bit about helps you understand if the resulting keyphrases are of quality. This requires the learning algorithm to generalize from the training data to unseen situations in a 'reasonable' way (see inductive bias). An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances.
A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). It infers a function from labeled training data consisting of a set of training examples. Dataįor this tutorial, we are going to be using a document about supervised machine learning: doc = """ Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
Can you search word documents for keywords how to#
Now, the main topic of this article will not be the use of KeyBERT but a tutorial on how to use BERT to create your own keyword extraction model. Instead, I decide to create KeyBERT a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings. What if we were to use BERT instead of statistical models?Īlthough there are many great papers and solutions out there that use BERT-embeddings (e.g., 1, 2, 3, ), I could not find a simple and easy-to-use BERT-based solution. BERT is a bi-directional transformer model that allows us to transform phrases and documents to vectors that capture their meaning. However, these models typically work based on the statistical properties of a text and not so much on semantic similarity. With methods such as Rake and YAKE! we already have easy-to-use packages that can be used to extract keywords and keyphrases. Keyword extraction is the automated process of extracting the words and phrases that are most relevant to an input text.
When we want to understand key information from specific documents, we typically turn towards keyword extraction.