Term-based indexing of documents is conventionally implemented by stemmers or their corpus-based improvements, both of which encode implicit linguistic information. Terms are directly derived from document content such that a unique indexing approach is available at indexing run-time. For highly inflectional languages where term variation is high, such techniques are more error-prone. The main focus of the current study is the extraction and normalization of single terms and phrases and the proposal of authenticated control of indexing. The proposed approach relies on the use of explicit linguistic knowledge, appropriately encoded in large language resources. Such control guarantees the highest possible expansion factor for indexing terms as well as indexing consistency. Moreover, it offers a framework where different and eventually contradicting indexing criteria can be practiced, conventional and Natural Language Processing (NLP)-based Information Retrieval (IR) applications can be served, while adaptations can be made for tuning to a specific domain or corpus.

基于术语的文档索引通常由词干分析器或其基于语料库的改进实现，两者都对隐式语言信息进行编码。术语直接源自文档内容，因此在索引运行时可以使用独特的索引方法。对于术语变化很大的高度屈折变化的语言，此类技术更容易出错。当前研究的主要焦点是单个术语和短语的提取和规范化以及索引的认证控制的提议。所提出的方法依赖于使用明确的语言知识，适当地编码在大型语言资源中。这种控制保证了索引术语的最大可能扩展因子以及索引一致性。而且，可以提供基于NLP ) 的信息检索 ( IR ) 应用程序，同时可以进行调整以调整到特定领域或语料库。