Abstract: An automatic file clustering algorithm enables documents within a file system to be displayed in a semantic view. The file clustering algorithm maps all words and documents into an appropriate semantic vector space, clusters the documents at a predetermined level of granularity, and assigns a meaningful descriptor to each resulting cluster. The documents are displayed to the user in a hierarchy in accordance with the resulting clusters. This results in a virtual file system with a semantic organization, that allows the user to navigate by content.

Abstract: A multi-lingual indexing and search system performs tokenization and stemming in a manner which is independent of whether index entries and search terms appear as words in a dictionary. During the tokenization phase of the process, a string of text is separated into individual word tokens, and predetermined types of tokens are eliminated from further processing. The stemming phase of the process reduces words to grammatical stems by removing known word-endings associated with the various languages to be supported. Known word endings are removed from the word tokens without any effort to guarantee that the remaining stem is contained in a dictionary. In a preferred implementation, the stemming process is only applied to nouns.

Abstract: A multi-lingual indexing and search system performs tokenization and stemming in a manner which is independent of whether index entries and search terms appear as words in a dictionary. During the tokenization phase of the process, a string of text is separated into individual word tokens, and predetermined types of tokens are eliminated from further processing. The stemming phase of the process reduces words to grammatical stems by removing known word-endings associated with the various languages to be supported. Known word endings are removed from the word tokens without any effort to guarantee that the remaining stem is contained in a dictionary. In a preferred implementation, the stemming process is only applied to nouns.

Abstract: A multi-lingual indexing and search system performs tokenization and stemming in a manner which is independent of whether index entries and search terms appear as words in a dictionary. During the tokenization phase of the process, a string of text is separated into individual word tokens, and predetermined types of tokens are eliminated from further processing. The stemming phase of the process reduces words to grammatical stems by removing known word-endings associated with the various languages to be supported. Known word endings are removed from the word tokens without any effort to guarantee that the remaining stem is contained in a dictionary. In a preferred implementation, the stemming process is only applied to nouns.

Abstract: A multi-lingual indexing and search system performs tokenization and stemming in a manner which is independent of whether index entries and search terms appear as words in a dictionary. During the tokenization phase of the process, a string of text is separated into individual word tokens, and predetermined types of tokens are eliminated from further processing. The stemming phase of the process reduces words to grammatical stems by removing known word-endings associated with the various languages to be supported. Known word endings are removed from the word tokens without any effort to guarantee that the remaining stem is contained in a dictionary. In a preferred implementation, the stemming process is only applied to nouns.