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G Component
George Heidorn spearheaded the development of a
programming language called "G" (-short for "Gamma", and also for "Grammar" or "George" J). G has a lot in common with the AI language LISP, except that it includes specialized structures for representing the linguistic relationships. G, along with MIND, enabled the NLP Group to transform their conceptual dreams of an NLP system into the reality of a working program, eventually known as NLPWin.

Microsoft English Grammar (MEG) Component

Karen Jensen, a leading authority on English grammar,
accomplished the awesome task of creating a comprehensive set of English grammatical rules, using the G language. These rules, called the Microsoft English Grammar (MEG), form the basis of the NLPWin Sketch component. The Sketch module parses text to produce syntactic structures, which are passed to the next component in the system. "The beauty of NLPWin is that any ambiguity is retained and passed up to the next level for resolution there or beyond," says Jensen.

Portrait Component

Lucy Vanderwende oversaw the construction of
NLPWin's next stage, Portrait, which uses semantic information automatically extracted from the definitions and example sentences in MIND, to determine correct phrasal attachment during parsing. In other words, the Sketch component does not attach prepositional phrases, but the Portrait component does.

Logical Form Component

Vanderwende also played a significant role in the
development of the Logical Form component. This module

encodes the abstract relations between the concepts in a sentence. "Many of these relationships can be captured using a small set of semantic relationships between a head word and its modifiers," says Vanderwende.

Perhaps the single biggest challenge in developing
NLPWin was creating the method for storing the mapping of the complex and abstract relationships among words. Although a group effort, Bill Dolan originated the conceptual framework for a semantic network. It had to be capable of representing the inter-linking relationships between the logical forms (grammatical relationships) among words parsed from machine-readable dictionaries and other sources.


Heidorn and Richardson lead the way in turning this
theoretical structure into a working code base. The auto-construction of semantic nets was not a new idea in the early 1990s. However, building a program that self trained from a variety of language sources and retained the ambiguity in natural language, critical for discovering the meaning of words, was a radical concept. After years of experimentation, and a number of breakthroughs, the NLP Group finally developed the means to auto-construct a semantic net capable of accomplishing both requirements and called it MindNet.
Figure 2 illustrates the conceptual view of how words
interlock in MindNet. For example, the word bird maps to Hawk through the is_a relationship. Duck also interlocks with bird by the same is_a relationship. By sliding along these relationships, NLPWin uses the knowledge stored in MindNet to identify the meaning of words in relations to other words.

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