Programming
In an earlier post, I showed how Prolog can be used to generate poetry, making use of my “iLexicon”. I want to continue the discussion today by giving another example, this time based on the theme of sounds emitted by various animals and birds. As hinted in my previous articles, “iLexicon” stands for “Intelligent Lexicon”. […]
One of the nice features of Sicstus Prolog is the support for storing Terms externally in a Berkeley DB database. Since we can control how the terms are indexed in the database, it is possible to store and retrieve a large amount of Terms (the limit is 2^32-1) fairly efficiently. This can be useful when we work in a […]
In an earlier article, I showed how we can generate poetry (with limitations, of course!) using my iLangGen framework. That implementation (in Lisp) made use of iLexicon, a large dictionary of English words, which I have been building over the years. I subsequently ported iLexicon to Prolog and it now works well in SICStus Prolog. […]
This is the third part in the series on information extraction from unstructured text. In the first part, we saw how MeaningCloud allows us to specify complex rules to identify custom categories through their Deep Categorization API. The second part covered spaCy’s pattern matcher. Today, I would like to discuss how we can use TextRazor’s “Prolog Engine” to perform […]
In the previous article, I explored the Deep Categorization capabilities of MeaningCloud. We saw how a powerful rule-based pattern matching language allowed us to map fragments of unstructured text to custom categories. In today’s post, I want to go through spaCy’s pattern matching capabilities. The version I am using is 2.0.13. Some newer features are available […]
MeaningCloud announced their premium offering “Deep Categorization” service quite recently. You can read about it in this nice blog by Blanca Galego. Compared to the canonical classification models that rely on machine learning using large data sets, MeaningCloud’s deep categorization models use hand-crafted rules that take advantage of “morphosyntactic, semantic and contextual information” to classify a piece […]
In an earlier article, I had described Python integration in Mathematica 12. In addition to Python, NodeJS is also supported as a default “external” language. In today’s article, I will focus on NodeJS integration. By the way, NodeJS support was introduced in Mathematica 11.2. Before using NodeJS with Mathematica 12, the system must be configured […]
One of the more recent additions to the core Wolfram Language is the “Curry” function. It was introduced in Mathematica ver 11.3 and is still (as of ver 12.0) considered Experimental. In this post, I would like to go over this function briefly. Non-curried functions in Mathematica have to be invoked with the arguments (both […]
Mathematica has had Python support since ver 11.2 through ExternalEvaluate[]. In ver 11.3 it was possible to input Python expression in a cell by beginning with “>” character. The good news is that Mathematica 12 has significantly enhanced this integration. Python Cells Let us start with the simplest way to use Python code in Mathematica. […]
![TextContents[ ] Function in Mathematica 12](http://i0.wp.com/www.rangakrish.com/wp-content/uploads/2019/04/FileImport.png?resize=675%2C34&ssl=1 "TextContents[ ] Function in Mathematica 12")
Mathematica 12 was released a few days ago. It has been over a year since version 11.3 came out in March 2018. The long wait appears justified since the new release boasts of numerous improvements and new features across several areas. You may want to read this blog post by Stephen Wolfram. In the area of […]
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