Category Archives: Learning
10 May Word Structure Analysis
Analyzing Morphology In many natural languages, gender or case has a profound affect on morphology – so much so that students have to memorize conjugation tables. It’s really easy to teach computers about conjugation tables and they remember very well. If it were not so, it may be difficult to keep their attention long enough […]
09 May Stratum Morphology
Morphology Morphology is about what happens to words to change their structure, impacting their meaning and usage. In English, we add -s or -es at the end of words to make them plural (guy –> guys, time –> times). Japanese, on the other hand, uses reduplication (hito –>hitobito, toki –> tokidoki) to make words plural. Adding to words, affixation, has three […]
08 May Three-Dimensional Model of Language
Topographical maps of concepts in a text provide useful views of language. Fortuna et al in Semantic Knowledge Management (pp. 155-169) describe how three-dimensional topic maps can both give meaningful insights into clusters of related content, such as news stories or published papers. I have frequently stressed the importance of concept associations in the brain, in cognitive […]
07 May Pairs of Language Strata
The Paired Model By pairing language strata, we attempt to find or describe symmetrical structures in language, thus helping clarify one of the most abstract phenomena known to man: verbal communication. This pairing of characteristics is also useful in decomposing the problem into smaller chunks to make it easier for computers to deal with. A note […]
06 May Impulse Waves in Layers
Layered Model Just as the brain has areas with three to six distinct layers, a typical artificial neural systems (ANS) also has several layers. The example at right shows a network with three layers that illustrate a neural network‘s distributed architecture. The uniform circles connected by lines are symbolic of the state of an ANS at […]
05 May Learning from Errors
If at first you don’t succeed, try – try again. Humans are pretty good at learning from our mistakes. In fact, some suggest that whatever doesn’t kill you makes you stronger. Today I’d like to riff on that theme a bit, and talk about ways in which machines can implement learning from errors. Error Minimization […]
05 May A Slice of Language
A Different Approach The previous section explained why we need the ground-up formulation of a new grammar for Natural Language (NL) understanding: none of the existing ones can accommodate the high demands of modern technology and approximate the paradigm of artificial intelligence as a model for human linguistic competence and performance across all linguistic phenomena. In […]
03 May Up from Words to Sentences
Words and Sentences The game of Scrabble is completely about words. Crossword puzzles go from sentence or phrase to word. Our analytical approach begins with the word, but doesn’t stop there. From both the cognitive and computational perspectives, the sentence exhibits far more complexity and changeability than the word. At any given moment, the structure of […]
02 May Linguistic Building Blocks
Linguistic Building Blocks While languages are infinite, each has a finite number of structures, functions and attributes. Functions and attributes are the building blocks of a grammar. Grammars or languages are categorized as regular, context-free (CF), context-sensitive (CS), recursive, and recursively enumerable. A context-sensitive grammar is a powerful formalism that describes the language in terms of […]
01 May Traditional Grammar from the Top Down
What is Traditional Grammar Grammars provide the knowledge and rules necessary to understand or disambiguate the often ambiguous strings of words that constitute language. People disambiguate by searching available knowledge (Nirenburg, 1987). Because a traditional grammar specifies a finite set of rules or patterns which attempt to capture the regularities of a language (Grosz, 1986), it […]