Next generation multi-media search engine based on soft computing

语言歧义是计算机处理词义最困难的问题之一。词义随着语境的变化而动态变化。我们必须指定上下文来识别它们。这里我们提出了一种用概念模糊集来表示这些意义的方法。首先，我们使用改进的虚构模型(一种类似于n元语法模型的预测方法)和目标词之前的单词序列来自动生成原子概念模糊集。然后，我们使用原子模糊集和基于共现的关系生成依赖于上下文的概念模糊集。在我们的实验中，我们使用了一个由100万个Newswire文本数据组成的大型语料库。这些任务的结果表明，我们的方法在生成依赖于上下文的概念模糊集方面是有效的。

Ambiguity in language is one of the most difficult problems in dealing with word senses using computers. Word senses vary dynamically depending on context. We must specify the context to identify them. We propose here a method to represent such senses using conceptual fuzzy sets. First, we used the modified confabulation model (a prediction method similar to the n-gram model) and word sequences just before the target word to generate atomic conceptual fuzzy sets automatically. Then we generated conceptual fuzzy sets depending on context using the atomic fuzzy sets and a relationship based on cooccurrences. We used a large corpus consisting of 1 million newswire text data in our experiments. The results of these tasks demonstrated that our methodology was effective to generate conceptual fuzzy sets depending on context.