Discovering Semantic Relations Between Words

Understanding language in any form requires understanding connections among words, concepts, phrases and thoughts. Many of the problems we face today in artificial intelligence depend in some way on understanding this network of relationships, which represent the facts that each of us knows about the world and how words relate to one another. When people communicate with each other, their conversation relies on many basic, unspoken assumptions, and they often learn the basis behind these assumptions long before they can write at all. Only traces of these assumptions are found in corpora.
Generative Lexicon and Common Sense Reasoning are related areas which are focused on learning such word meanings. This paradox of this vital unspoken information has lead to researchers in these fields to manually construct resources either using experts or volunteers from the internet. Such projects are major undertakings, using vast amounts of time and resources, but the resulting resources are invaluable to the field.
In my work, I hope to create a method, using principal component analysis, which can aid in the creation and bootstrapping of a large lexical resource by providing the human ontologist or volunteer contributer with a list of possible possible relations for each word in the system.

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