In the previous part, we discussed how you can take advantage of spaCy’s rule-based matcher engines, using both pattern rules and documents. In this part, you’ll see some examples of the former, based on using linguistic features, such as syntactic dependency labels and simple and extended part of speech tags. Determining Manually a Sequence of Words Satisfying a Pattern Rule...
Tag: Python
Using spaCy to Find Sequences of Words Based on Pattern Rules – Part 1
spaCy’s rule-based matcher engines are much powerful than just using regular expressions, since those engines allow you to find not only particular words in a submitted text, but also discover their relationships. spaCy provides you with the ability to match sequences of tokens based on both pattern rules and documents. In the first case, you use spaCy’s Matcher object. For...
Using Linguistic Features in NLP
If you had to describe what NLP is about, what would you point out as the most important thing? Is there something that is vital for many use cases and is employed in almost every NLP application? For example, what do you almost certainly need when it comes to a task of information extraction of any kind? Right. Linguistic features,...
Using Linguistic Features in NLP
If you had to describe what NLP is about, what would you point out as the most important thing? Is there something that is vital for many use cases and is employed in almost every NLP application? For example, what do you almost certainly need when it comes to a task of information extraction of any kind? Right. Linguistic features,...
Converting Raw Text into SQL Data with spaCy
As the title suggests, this article covers how raw text can be converted into structured data. Before going any further, a word on the terminology used here is needed. In simple terms, raw text can be thought of as a set of tokens grouped into sentences, carrying information expressed in a natural language such as English. Put simply, raw text...