Natural Language Processing
In my earlier article, I showed how the iLexicon system can generate verb conjugations based on <Verb, Tense, Person, Number> quadruple. For the 16 tense forms discussed in that article, the verb conjugations were generated in “active” voice. We all know that “transitive” verbs can be expressed in both “active” and “passive” voice. For example, […]
After my last book review, I decided to check out a few websites that claim to simplify English text and/or help compute the measure of readability. In today’s post, I am sharing the results of my experiment. www.simplish.org This site has some interesting functionality. It does spelling check, grammar check, text simplification, and can even […]
Title: Automatic Text Simplification Author: Horacio Saggino Publisher: Morgan & Claypool Publishers Year: 2017 Automatic Text Simplification is an active area of research in NLP and has been going on for over 20 years. The idea is to transform a given text T1 into text T2 such that T2 is easier to read and understand […]
In the previous article, I showed how we can use ATNs for extracting key information from natural language text. I also pointed out in that article that Definite Clause Grammars (DCG) are a more compact formalism for doing this. That will be the focus of today’s article. For a nice introduction to DCG, read this. […]
After Wood’s paper [1], Augmented Transition Networks (ATN) became popular in the 1970s, for parsing text. An ATN is a generalized transition network with two major enhancements: Support for recursive transitions, including jumping to other ATNs Performing arbitrary actions when edges are traversed Remembering state through the use of registers See the “Further Reading” section at […]
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”. […]
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. […]
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