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In the last article, I showed how to generate verb conjugations in the “iLexicon” system. Today, let us see how this idea can be extended to English “phrasal verbs”. According to Wikipedia: “a phrasal verb is a phrase such as turn down or ran into which combines two or three words from different grammatical categories: […]
We have been taught in school that English language has different “tense” forms. To help you quickly recollect, I am listing them in the table below (the verb ‘sleep’ is used as example): No. Tense Form Example Sentence (3rd Person Singular Pronoun) 1 Simple Present She sleeps 2 Simple Past She slept 3 Simple Future […]
Working with word patterns can be an exciting (and challenging) creative activity. Such patterns come into picture while playing word games, solving word puzzles or even writing poetry. It is precisely to facilitate such tasks that I am building my “iLexicon” system. One popular word game goes like this: The first player utters a word, […]
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 […]
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